Auto playlist generator7024424Abstract A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item. Claims What is claimed is: Description TECHNICAL FIELD
In Table I, the similarity values fall within the range 0 to 1.0. Examining a first entry, 1.00000 1111111, the difference vector 1111111 indicates that the seed item feature vector 510 matched the candidate item feature vector 520 in each attribute, thus, each bit of the difference vector was set to one, and the corresponding similarity value 1.00000 indicates a high degree of similarity between the two items. Examining a second entry, 0.06834 0111011, the difference vector 0111011 indicates that the seed item feature vector 510 matched the candidate item feature vector 520 in five attributes (rhythm description, rhythm type, vocal code, subgenre, and genre), and that the corresponding similarity value is 0.06834. Similarly, a third entry 0.04052 1110011 also indicates that the seed item feature vector 510 matched the candidate item feature vector 520 in five attributes (mood, rhythm description, rhythm type, subgenre and genre), and that the corresponding similarity value is 0.04052. Thus, the machine learning that was applied to the feature vectors and/or difference vectors determined that the combination of five matched attributes coded in the difference vector 0111011 should result in a higher similarity value than the five matched attributes coded in the difference vector 1110011. While one table of difference vectors and similarity values are described in connection with FIG. 5, it is to be appreciated that other such tables may be employed in accordance with the present invention, where such tables are hand coded, machine coded and/or the result of machine learning algorithms. It is to be further appreciated that although difference vectors and feature vectors with seven attributes are discussed in connection with FIG. 5, that difference vectors and/or feature vectors with a greater or lesser number of attributes, and/or with different attributes may be employed in accordance with an aspect of the present invention. The system in FIG. 5 operates on one seed item, while the system in FIG. 4 can operate on more than one seed item. There are various ways of combining multiple similarity scores into one similarity score. For example, the similarity score between every seed item and a candidate item can be averaged together to yield a single similarity score that would be used by list generation subsystem 428. It is to be appreciated that other techniques, such as weighted average, or median, can also be used. Tables such as table I can be generated using the following machine learning algorithm. Let Kij be defined by a training set of albums (or playlists) such that ##EQU1## where fn(si) is defined to be 1 when song si appears on the nth album (or playlist), 0 otherwise. It is to be appreciated that the matrix Kij is sparse, and hence can be stored and manipulated as a sparse matrix. The weights in the lookup table can be computed by first solving the sparse quadratic program ##EQU2## subject to the constraints that all αk are non-negative. The αk become a second layer of trainable weights, and the ψk are the first-layer of fixed basis functions, defined to be 1 if Akl is 0 or xil has the same value as xjl for all 1, where Akl is further defined to be the ith bit of the binary representation of the number k, and where xil is the lth metadata feature of the song si. In the case of the metadata feature vectors described above, l will range from 1 to 7 and k will range from 0 to 127, although other metadata feature vectors and other ranges are possible. Solving sparse quadratic programs is known in the art. The two-layer system can be represented in a lookup table, such as that shown in Table 1, by computing Lm (the mth lookup table entry) via ##EQU3## where gkm is defined to be 1 if Akl is zero or Aml is 1 for all l, and 0 otherwise. It is to be appreciated that other machine learning algorithms can be used to generate such lookup tables, or the lookup table can be hand-designed. In view of the exemplary systems shown and described above, methodologies that may be implemented in accordance with the present invention, will be better appreciated with reference to the flow charts of FIGS. 6 through 8 and 10. While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the present invention is not limited by the order of the blocks, as some blocks may, in accordance with the present invention, occur in different orders and/or concurrently with other blocks from that shown and described herein. Moreover, not all illustrated blocks may be required to implement a methodology in accordance with the present invention. The invention may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments. FIG. 6 illustrates a method 600 for generating a list. At 610, one or more candidate user items related to a seed item are identified. Such identification can be achieved by methods including, but not limited to, examining unique identifiers. At 620, similarity data that characterizes the similarity between a candidate user item and the seed item is produced. Such similarity processing can include, but is not limited to, feature vector processing, as described in connection with FIGS. 10 through 12. At 630 a list of one or more user items related to the seed item is produced. In one example of the present invention, identifying the candidates, producing the similarity data and producing the list are performed at a client component. The method illustrated in FIG. 6 is a high level method, which will now be described in greater detail. FIG. 7 illustrates a method 700 for producing a list. At 710, general initializations occur. Such initializations include, but are not limited to, allocating memory, establishing pointers, establishing data communications, acquiring resources, setting initial values for variables and displaying process activity. At 720, seed item data is input. Such data may include, but is not limited to, at least one seed item and/or at least one seed item identifier (e.g., a GUID or a database table index). At 725, the seed item(s) and/or the seed item identifier(s) can be employed to retrieve seed item descriptive metadata. The processing at 725 may retrieve a complete set of descriptive metadata or an incomplete set of descriptive metadata may be retrieved. If incomplete descriptive metadata is retrieved, modified processing will be required at 740 to compare the seed item(s) to the user item. At 730, candidate item descriptive metadata is retrieved. Again, the retrieval at 730 may yield a complete set of metadata for the candidate item or it may yield an incomplete set of metadata for the candidate item. If an incomplete set of metadata is retrieved, then modified processing may be required to compare the seed item to the candidate item. At 740, the seed item descriptive metadata is compared to the candidate item descriptive metadata. As explained above, such comparison may be between complete sets of metadata for the seed item and the candidate item or between complete and/or incomplete sets of descriptive metadata. One possible method for comparing the seed item descriptive metadata and the candidate item descriptive metadata is to form feature vectors from the descriptive metadata. Other comparison techniques known in the art may also be employed in accordance with the present invention. One possible feature vector comparison technique is described in connection with FIGS. 11 and 12. At 750, a similarity score is produced. The similarity score represents the degree to which the seed item descriptive metadata is related to the candidate item descriptive metadata. One possible method for producing a similarity score is described in connection with FIG. 12. Although FIG. 12 refers primarily to looking up a similarity score related to a difference vector, it is to be appreciated that other similarity score computations (e.g., a similarity function) may be employed in accordance with the present invention. At 760, a determination is made concerning whether another candidate item is available to be compared to the seed item. If the determination at 760 is YES, then processing continues at 740, otherwise processing proceeds to 770. At 770, a list is generated. The list will be based, at least in part, on the similarity scores computed at 750. For example, the similarity scores of 750 may be stored and sorted, and then at 770 the top Y items (e.g., 30) may be added to the list. Similarly, the similarity scores of 750 may be stored and sorted, and then at 770 a random assortment of items related to the similarity scores above a threshold value may be included in the list. At 780, a determination is made whether another seed item remains to be processed. If the determination at 780 is YES, then processing continues at 720, otherwise processing concludes. Whereas the method 700 dealt with generic user items, FIG. 8 illustrates a method 800 for generating a playlist of songs. At 810, general initializations occur. Such initializations include, but are not limited to, allocating memory, establishing pointers, establishing data communications, acquiring resources, setting initial values for variables and displaying process activity. At 820, seed song data is input. Such data may include, but is not limited to, at least one song and/or at least one song identifier (e.g., a GUID, a database table index). A song identifier can include, but is not limited to, information concerning the song artist(s), the song name and the album on which the song appeared. At 825, the seed item(s) and/or the seed item identifier(s) can be employed to retrieve seed item descriptive metadata. In one example of the present invention, the song descriptive metadata includes genre information, subgenre information, style information, mood information, vocal coding information, rhythm type information and rhythm description information. The genre information provides a way to classify a song. For example, a song may be identified as being in a genre (e.g., metal, punk, alternative, folk, reggae). While five genres are identified above, it is to be appreciated that a greater or lesser number of genres may be employed in accordance with the present invention. Similarly, the subgenre information can be employed to further classify a song. For example a song may be characterized as being in a subgenre (e.g., punk, new punk, before and beyond punk). While three subgenres are identified above, it is to be appreciated that a greater or lesser number of subgenres may be employed in accordance with the present invention. Style information can similarly be employed to further describe a song. For example, a song may be classified as being of a certain style (e.g., east coast rap, west coast rap, jazz rap, gangsta rap). While four styles are identified above, it is to be appreciated that a greater or lesser number of styles may be employed in accordance with the present invention. Likewise, mood information can be employed to describe a mood for a song (e.g., dreamy, soothing, fun, depressing). While four moods are identified above, it is to be appreciated that a greater or lesser number of moods may be employed in accordance with the present invention. Additionally vocal coding information (e.g., female, female/female, male, male/male, instrumental), rhythm type information (e.g., straight, shuffle, swing, disco) and the rhythm description information (e.g., frenetic, steady, throbbing, free) can be employed to describe a song. While five vocal codings, four rhythm types and four rhythm descriptions are identified above, it is to be appreciated that a greater or lesser number of such items may be employed in accordance with the present invention. The processing at 825 may retrieve a complete set of descriptive metadata or an incomplete set of descriptive metadata. For example, of the seven descriptive metadata attributes identified above, information concerning only five may be available, or, no descriptive metadata for the seed song may be available. In such a case, modified processing will be required at 840 to compare the seed song to the user song. At 830, candidate song descriptive metadata is retrieved. In one example of the present invention, descriptive metadata substantially similar to the metadata described in connection with the seed song(s) of 825 is retrieved. Again, the retrieval at 830 may yield a complete set of metadata for the candidate song or it may yield an incomplete set of metadata for the candidate song. If an incomplete set of metadata is retrieved, then modified processing may be required to compare the seed song to the candidate song. In cases where the seed song metadata is incomplete and the candidate song metadata is incomplete, then a third case of modified processing may be required. At 840, the seed song descriptive metadata is compared to the candidate song descriptive metadata. As explained above, such comparison may be between complete sets of metadata for the seed song and the candidate song or between complete and/or incomplete sets of data. One possible method for comparing the seed song descriptive metadata and the candidate song descriptive metadata is to form feature vectors from the descriptive metadata. Other comparison techniques known in the art may also be employed in accordance with the present invention. One possible feature vector comparison technique is described in connection with FIGS. 10 through 12. At 850, a similarity score is produced. One possible method for producing a similarity score is described in connection with FIG. 12. Although FIG. 12 refers primarily to looking up a similarity score related to a difference vector, it is to be appreciated that other similarity score computations (e.g., a similarity function) may be employed in accordance with the present invention. The similarity score is a measure of the similarity of two songs. In one example of the present invention, the similarity scores range between 0.0 (completely dissimilar) to 1.0 (very similar). Thus, a user may specify a desired similarity range when requesting a playlist. By way of illustration, the user may want similar songs (e.g., scores between 0.04 and 0.07) included in the playlist but may not want very similar songs (e.g., score above 0.07) included in the playlist. By way of further illustration, a user may desire a wide variation of songs in their playlist and thus the user may want songs with scores below 0.03, for example. At 860, a determination is made concerning whether another candidate song is available to be compared to the seed song. If the determination at 860 is YES, then processing continues at 840, otherwise processing proceeds to 870. At 870, a playlist is generated. Which songs are included in the playlist will be based, at least in part, on the similarity scores computed at 850. For example, the similarity scores of 850 may be stored and sorted, and then at 870 the top Y items (e.g., 30) may be added to the list. Similarly, the similarity scores of 850 may be stored and sorted, and then at 870 a random assortment of songs related to the similarity scores above a threshold value may be included in the list. As described in connection with 850, user determined thresholds may be employed to determine which songs to include in the playlist. Alternatively, processing candidate items can stop when a desired number of songs above a threshold have been found. Thus, a random subset of candidate items can be considered. At 880, a determination is made concerning whether another set of seed item(s) remains to be processed. If the determination at 880 is YES, then processing continues at 820, otherwise processing concludes. FIG. 9 is a flow chart that illustrates one method employed with the present invention. At 910, processing associated with the artist occurs, while at 920, processing associated with a track occurs. The following pseudocode implements the example method for identifying an item that a user desires to add to a media library. Identifying the user item to be added to the media library involves matching identifying data associated with the user item to identifying data associated with items already identified by an identifying system. Since the identifying data associated with the user item may not be entirely consistent with identifying data associated with items already processed into the media library, inexact matching of identifying data may be required. The method loops over reference tracks (e.g., tracks already processed into the media library by the identifying method) and the reference tracks "vote" for user items when the artist words and/or the track words in the reference track match the artist words and/or track words in the user item, where the vote indicates that the reference track believes that it matches the user item.
In this example method, the reference tracks are ordered by artist identifier. For efficiency concerns, artist voting occurs when the artist identifier is not the same as the previous reference track. The user song artist votes may be stored in a sparse array, which is cleared. The artist name of the reference track may then be split into artist words, and canonicalized by converting to lower case, etc. Such splitting and canonicalization may be required, because artist, song and album information may include one or more formatted words (e.g., artist: MEGA super STAR Dude; album: super (Duper) GrEaTeSt hits Album "4.1"; track: My BIG Hit). In one example of the present invention a word is defined as a word character followed by one or more word characters, quotation marks or periods. A word character is defined as a member of the set of the letters a through z, the letters A through Z, an underscore and the digits 0 through 9. It is to be appreciated that the definition of word and word character are but one possible definition, and that other definitions of words and/or characters that may be employed in accordance with the present invention are intended to fall within the scope of the invention (e.g., international word and character definitions). The words may be canonicalized by removing periods and quotes and converting uppercase letters to lowercase, for example. Then, for each artist word, elements in the user song artist vote array are incremented when a user artist name that contains that artist word is encountered. An artist word to user item map can be precomputed before voting to increase efficiency. After the artist words have been processed, the scores for the user songs that received at least one vote are computed. If the artist matching score is above a threshold (e.g., 0.7), and better than previous artist matching scores for that user item, the artist identifier of the user item is assigned to be the artist identifier of the reference track. For matching on track words, a subset of the user items is considered, those user items whose track names match the first N characters of the reference track name, N being an integer (e.g., 8). If no such user items match, then no voting takes place. Further processing is thus restricted to the matching user items. For efficiency, before voting, an index of words that occur in the user items with the same first N characters can also be formed. Assuming that at least one user item matched the first N characters of the reference track name, then the reference track name is split into track words and canonicalized, and the user song track vote sparse array is cleared. Then, for each track word, the method increments the user song track vote array elements that correspond to user items track names that contain the track word. After the voting is completed, the user song artist votes are added to the user song track votes, to form a count of the number of artist/track matching words for each user song. This count is then stored in the user song track vote sparse array. Then, for each song that had at least one track vote, the track matching score is computed, and if the track matching score is above a threshold (e.g., 0.6) and better than previous track matching scores for that user song, then the track identifier for the user song is set to the track identifier for the reference track. The matching described above may employ, for example, the following method. The number of unique exact matching words between the two artist names and the number of unique exact matching words between the two track names will be computed and stored (e.g., in a count variable MatchWords). The number of words in the first identifier that are not found in the second identifier will be counted and stored (e.g., in a count variable FirstUniques) and the number of words in the second identifier that are not found in the first identifier will be counted and stored (e.g., in a count variable SecondUniques). Thus, a matching score can be computed using, for example, an equation: MatchScore=MatchWords/(MatchWords+(FirstUniques+SecondUniques)/2) (1). It is to be appreciated that equation (1) is but one example equation that can be employed to match identifying information and that other such equations can be employed in accordance with the present invention. FIG. 10 further illustrates processing associated with 850 (FIG. 8). FIG. 10 illustrates one possible method for producing a similarity value based on comparing feature vectors to produce a difference vector that can be employed to retrieve a similarity value. At 850A, the seed song feature vector and the candidate song feature vector are compared. In one example of the present invention, the feature vectors include seven attributes, where each attribute may have one or more possible values. For example, a mood attribute may have twenty one possible values while a style attribute may have two thousand possible values. Blocks 850B, 850C and/or 850D may be performed for each feature attribute. Thus, at 850B, a determination is made concerning whether the seed song feature vector attributes match the candidate song feature vector attributes. If the determination at 850B is YES, then at 850C, the bit corresponding to the compared attribute in the difference vector is set to one. If the determination at 850B is NO, then at 850D, the bit corresponding to the compared attribute in the difference vector is cleared to zero. Thus, a difference vector that contains one binary digit for each attribute compared between the seed song feature vector and the candidate song feature vector is created. At 850E, a similarity value can then be computed based on the difference vector. By way of illustration and not limitation, a function that accepts as an input a difference vector and produces as an output a similarity value may be employed to produce the similarity value. By way of further illustration and not limitation, a value may be retrieved from a database table by employing the difference vector as an index into the database table. While a similarity value function and a similarity value lookup are described above, it is to be appreciated that other methods for producing a similarity value from a difference vector may be employed in accordance with the present invention (e.g., treat the difference vector as a binary number corresponding to the similarity value). FIG. 11 is a schematic block diagram illustrating feature vectors being compared to produce a difference vector 1130. A first feature vector 1110 may include one or more feature attributes (e.g., attributes 1110A through 1110R, R being an integer). For example the first feature vector 1110 may include a genre attribute 1110A and a subgenre attribute 1110B that can be employed to facilitate characterizing a song. Similarly, a second feature vector 1120 may include one or more feature attributes (e.g., attributes 11120A through 1120S, S being an integer). For example the second feature vector 1120 may include a genre attribute 1120A and a subgenre attribute 1120B that can be employed to facilitate characterizing a song. The feature vectors may be compared attribute by attribute to produce a difference vector 1130 that contains a binary digit that codes information concerning whether the feature attributes matched. Such attribute by attribute comparison may be complicated by a situation where the feature vectors contain different attributes and/or a different number of attributes. For example, the first feature vector 1110 might contain a first number R of feature attributes while the second feature vector 1120 might contain a second number S of feature attributes. Thus, one or more bits in the difference vector 1130 may code information based on a comparison of feature attributes that do not have a one to one correspondence. By way of illustration, the first feature vector 1110 may code information concerning song length into two attributes (e.g., length of song in seconds, size of song in kilobytes) while the second feature vector 1120 may code information concerning song length into one attribute (e.g., play length). Thus, setting or clearing a bit in the difference vector 1130 may involve resolving the dissimilar feature vector attributes. More specifically, if a subset of feature vectors is missing at random, the difference vector for the missing data can be set to be "1", indicating a difference. FIG. 12 is a schematic block diagram illustrating a difference vector 1210 being employed to produce a similarity value 1240. The difference vector 1210 can be accepted as an input by a similarity value generator 1220. In one example of the present invention, the similarity value generator 1220 and the difference vector value store 1230 reside on a client machine. The similarity value generator 1220 may then retrieve a similarity value from a difference vector value store 1230. For example, for a four bit difference vector 1210, the similarity value generator 1220 may employ the four bit difference vector 1210 as an index into the difference vector value store 1230. In such a case, the difference vector value store 1230 may contain a table similar to that presented in Table II below.
Table II may have been generated by hand or it may have been generated through machine learning techniques applied to analyze testing data associated with feature vector comparison. Furthermore, Table II may not be a static entity. For example, the present invention may include a user interface that includes a button that allows a user to inform the list generator that a first song (e.g., Disco Duck) is never to be played in association with a second song (e.g., Stairway to Heaven) while a third song (e.g., Misty Mountain Hop) is always to be played in association with the second song. Thus, the present invention facilitates manipulating the production of a difference vector so that when Disco Duck is compared to Stairway to Heaven, a difference vector that will yield the lowest possible similarity score is produced while when Misty Mountain Hop is compared to Stairway to Heaven a difference vector that will yield the highest possible similarity score is produced. In this way the present invention is adaptable to user generated overrides of computed song similarities. FIGS. 13, 14 and 15 illustrate three different client/server configurations that may be employed with the present invention. It is to be appreciated that other configurations may be employed in accordance with the present invention. Note that in each of FIGS. 13, 14 and 15, there is an item identifying system, a list generating system and a reference metadata data store, which cooperate to facilitate generating lists of user items, where metadata associated with the user items is stored in the reference metadata data store. FIG. 13 illustrates a client-server system 1300 where reference metadata 1330 is transmitted from a server 1310 to a client 1320. Transmitting the reference metadata 1330 to the client 1320 facilitates list processing (e.g., identifying computations, similarity computations) on the client 1320, which can reduce the amount of processing performed on the server 1310, providing advantages over conventional server based systems. For example, in a conventional server-based system, if one hundred thousand users substantially simultaneously request a list, response times may not be acceptable since the processing may proceed serially at the server 1310. However, by employing the present invention, if the one hundred thousand users had previously received the descriptive metadata 1330, then the response times may be acceptable because the processing may proceed substantially in parallel at the client machines associated with the one hundred thousand users. In one example of the present invention, the reference metadata 1330 can include substantially all the reference metadata available on the server 1310. By transmitting substantially all the reference metadata available on the server 1310 to the client 1320, the number of communications between the client 1320 and the server 1310 may be reduced. By scheduling the transmission of the reference metadata 1330 to the client 1320 at a time of low network traffic, network congestion problems and delays associated with conventional systems can be mitigated. Furthermore, by transmitting substantially all the reference metadata 1330 to the client 1320, processing performed at the server 1310 is reduced, mitigating server congestion problems associated with conventional systems. Further still, by transmitting substantially all the reference metadata 1330 to the client 1320, the client 1320 is not required to reveal information to the server 1310 that the client may consider private (e.g., songs owned by the user). Thus, privacy problems associated with conventional systems are mitigated. But if the user so chooses, the user may transmit information from the client 1320 to the server 1310 identifying items about which the client 1320 would like to receive reference metadata (as illustrated in FIG. 14). Thus, the amount of reference metadata 1330 transmitted from the server 1310 to the client 1320 may be reduced, further mitigating congestion problems associated with conventional systems. With substantially all of the reference metadata 1330 available on the client as the reference metadata 1322, a client-side item identifying system 1326 can be employed to generate as-added metadata associated with a new user item that a user desires to add to the user item library 130. Similarly, with substantially all of the reference metadata 1330 available on the client as the reference metadata 1322, a client-side list generating system 1324 can be employed to generate a list of items related to a seed item provided to the client. FIG. 14 illustrates another client-server system 1400 where a server 1410 transmits a subset 1430 of reference metadata 1412 stored on the server 1410 to a client 1420. The subset 1430 may be related to one or more user items 1440 about which the client 1420 informs the server 1410. The user items 1440 may be processed by an item identifying system 1414 that resides on the server 1410 and which reconciles identifying information associated with the user items 1440 to a schema employed to identify, store and/or retrieve information stored in the reference metadata 1412. The subset of reference metadata 1430 can thus be employed by a list generating system 1422 on the client 1420 to produce a list of user items related to a seed item. The client server configuration in FIG. 14 facilitates server-based data mining. FIG. 15 illustrates yet another client server system 1500 where the reference metadata 1512, the item identifying system 1516 and the list generating system 1514 all reside on a server 1510. In this configuration, the client 1520 sends information concerning one or more user items 1530 to the server 1510. The information concerning the user items 1530 is then processed by the item identifying system 1516 to produce unique identifiers that can be employed, for example, to locate user items and/or metadata associated with user items. When the client 1520 sends seed item(s) 1540, and/or information concerning seed item(s) 1540 to the server 1510, the server 1510 can run the list generating system 1514 at the server and return a list 1550 of items related to the seed item 1540 to the client 1520. The client server configuration in FIG. 15 minimizes the amount of processing required on the client 1520. FIG. 16 is a flowchart illustrating a method 1600 for generating a final list. The following segment of pseudocode can be employed to implement the method illustrated in the flowchart associated with generating a final list via generating similarity values. At 1610, the similarity between the Cartesian product of a seed list and a user list is computed. At 1620, the mean similarity over that Cartesian product is computed. At 1630, the similarity list is generated and sorted. If seed song has track id then
Else if seed song has artist id then
Else
Endif For each user song with track or artist id
End for Sort final list by decreasing similarity Return final list In the case where the seed song has a known track identifier, then the seed list consists of the seed song. Otherwise, if the seed song has a known artist identifier, then the seed list consists of user items whose artist identifier matches the seed song artist identifier. Otherwise, if the seed song is unknown, the final similarity list will include those user items whose artist name matches the seed song artist name. If there is more than one seed song, the seed list can be the concatenation of the seed list for all of the individual songs that have known artist or track identifiers. A user list is computed analogously for user songs that contain a track identifier or artist identifier. If the user song has a track identifier, then the user list is the user song. Otherwise, the user list is user songs whose artist identifier matches the artist identifier of the user song under consideration. For efficiency concerns, in one example of the present invention, limits may be placed on the Cartesian product between the seed list and the user list. If one of the two lists contains only a single song, iteration over the other list is limited to a pre-determined, configurable threshold (e.g., to 200 items). If both lists contain more than one song, then iteration over both lists is similarly limited to a pre-determined, configurable threshold (e.g., to 30 items). For further efficiency concerns, in one example of the present invention, when a user song does not have a track identifier, the similarity between the seed song(s) and the user song is cached, so that if another user song has the same artist identifier but a missing track identifier, then the cached value may be retrieved and employed. In order to provide additional context for various aspects of the present invention, FIG. 17 and the following discussion are intended to provide a brief, general description of one possible suitable computing environment 1710 in which the various aspects of the present invention may be implemented. It is to be appreciated that the computing environment 1710 is but one possible computing environment and is not intended to limit the computing environments with which the present invention can be employed. While the invention has been described above in the general context of computer-executable instructions that may run on one or more computers, it is to be recognized that the invention also may be implemented in combination with other program modules and/or as a combination of hardware and software. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, one will appreciate that the inventive methods may be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively coupled to one or more associated devices. The illustrated aspects of the invention may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. FIG. 17 illustrates one possible hardware configuration to support the systems and methods described herein. It is to be appreciated that although a standalone architecture is illustrated, that any suitable computing environment can be employed in accordance with the present invention. For example, computing architectures including, but not limited to, stand alone, multiprocessor, distributed, client/server, minicomputer, mainframe, supercomputer, digital and analog can be employed in accordance with the present invention. With reference to FIG. 17, an exemplary environment 1710 for implementing various aspects of the invention includes a computer 1712, including a processing unit 1714, a system memory 1716, and a system bus 1718 that couples various system components including the system memory to the processing unit 1714. The processing unit 1714 may be any of various commercially available processors. Dual microprocessors and other multi-processor architectures also can be used as the processing unit 1714. The system bus 1718 may be any of several types of bus structure including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The computer memory 1716 includes read only memory (ROM) 1720 and random access memory (RAM) 1722. A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within the computer 1712, such as during start-up, is stored in ROM 1720. The computer 1712 may further include a hard disk drive 1724, a magnetic disk drive 1726, e.g., to read from or write to a removable disk 1728, and an optical disk drive 1730, e.g., for reading a CD-ROM disk 1732 or to read from or write to other optical media. The hard disk drive 1724, magnetic disk drive 1726, and optical disk drive 1730 are connected to the system bus 1718 by a hard disk drive interface 1734, a magnetic disk drive interface 1736, and an optical drive interface 1738, respectively. The computer 1712 typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by the computer 1712. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer 1712. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. A number of program modules may be stored in the drives and RAM 1722, including an operating system 1740, one or more application programs 1742, other program modules 1744, and program non-interrupt data 1746. The operating system 1740 in the computer 1712 can be any of a number of commercially available operating systems. A user may enter commands and information into the computer 1712 through a keyboard 1748 and a pointing device, such as a mouse 1750. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a satellite dish, a scanner, or the like. These and other input devices are often connected to the processing unit 1714 through a serial port interface 1752 that is coupled to the system bus 1718, but may be connected by other interfaces, such as a parallel port, a game port, a universal serial bus ("USB"), an IR interface, etc. A monitor 1754, or other type of display device, is also connected to the system bus 1718 via an interface, such as a video adapter 1756. In addition to the monitor, a computer typically includes other peripheral output devices (not shown), such as speakers, printers etc. The computer 1712 may operate in a networked environment using logical and/or physical connections to one or more remote computers, such as a remote computer(s) 1758. The remote computer(s) 1758 may be a workstation, a server computer, a router, a personal computer, microprocessor based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1712, although, for purposes of brevity, only a memory storage device 1760 is illustrated. The logical connections depicted include a local area network (LAN) 1762 and a wide area network (WAN) 1764. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. When used in a LAN networking environment, the computer 1712 is connected to the local network 1762 through a network interface or adapter 1766. When used in a WAN networking environment, the computer 1712 typically includes a modem 1768, or is connected to a communications server on the LAN, or has other means for establishing communications over the WAN 1764, such as the Internet. The modem 1768, which may be internal or external, is connected to the system bus 1718 via the serial port interface 1752. In a networked environment, program modules depicted relative to the computer 1712, or portions thereof, may be stored in the remote memory storage device 1760. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. FIG. 18 is a schematic block diagram of a sample computing environment 1800 with which the present invention may interact. The system 1800 includes one or more clients 1810. The clients 1810 may be hardware and/or software (e.g., threads, processes, computing devices). The system 1800 also includes one or more servers 1830. The servers 1830 may also be hardware and/or software (e.g., threads, processes, computing devices). While FIG. 18 illustrates a client/server configuration, it is to be appreciated that other configurations (e.g., stand alone, widely distributed, peer-to-peer, etc.) may be employed in accordance with the present invention. The system 1800 includes a communication framework 1850 that can be employed to facilitate communications between the clients 1810 and the servers 1830. The clients 1810 are operably connected to one or more client data stores 1815 that can be employed to store information local to the clients 1810. For example, the client data stores 1815 may store user songs, user song identifying data and/or user song descriptive metadata. Similarly, the servers 1830 are operably connected to one or more server data stores 1840 that can be employed to store information local to the servers 1830. For example, the server data stores 1840 may store songs, song identifying data and/or song descriptive metadata. In one example of the present invention, descriptive metadata initially resides on one or more server data stores 1840 and is transmitted to the client data stores 1820 to facilitate client side list generation processing. What has been described above includes examples of the present invention. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the present invention, but one of ordinary skill in the art may recognize that many further combinations and permutations of the present invention are possible. Accordingly, the present invention is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim.
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