A mechanism for discerning user preferences for categories of provided content. A computer receives response data including a set of preference values that have been assigned to content items by content users. Output data is computed based on the response data using a latent factor model. The output data includes at least: an association matrix that defines K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the concept-preference matrix.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving input data that includes response data, wherein the response data includes a set of preference values that have been assigned to content items by content users, wherein the preference values are drawn from a universe of possible values, wherein said receiving is performed by a computer system; computing output data based on the input data using a first latent factor model, wherein said computing is performed by the computer system, wherein the output data includes at least: an association matrix that defines a set of K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept; displaying a visual representation of at least a subset of the association strengths in the association matrix and/or at least a subset of the extents in the concept-preference matrix. 2. The method of claim 1, wherein said computing output data includes one or more of the following: performing a maximum likelihood sparse factor analysis on the input data using the first latent factor model; performing a Bayesian sparse factor analysis on the input data using the first latent factor model. 3. The method of claim 1, wherein the content items are content items that have been viewed or accessed or used by the content users. 4. The method of claim 1, wherein the content items are content items that are made available to the content users by an online content provider. 5. The method of claim 1, further comprising: receiving user input from a content user, wherein the user input indicates the content user's extent of preference for an identified one of the content items; and updating the response data based on the user input. 6. The method of claim 1, wherein the content items include one or more of the following: movies or videos or audiobooks or articles or news items or online educational materials or documents or images or photographs. 7. The method of claim 1, wherein a column of the content-preference matrix is used to predict one or more content items which the corresponding content user is likely to have an interest in. 8. The method of claim 1, wherein said displaying the visual representation includes displaying a graph based on the association matrix, wherein the graph depicts the strengths of association between at least a subset of the content items and at least a subset of the K concepts. 9. The method of claim 1, wherein the universe of possible values consists of two elements, wherein the first latent factor model characterizes a statistical relationship between (WC)i,j and a corresponding preference value Yi,j of the set of preference values, wherein (WC)i,j denotes the (i,j)th entry of the product matrix WC, wherein W is the association matrix, wherein C is the content-preference matrix, wherein i is a content item index, wherein j is a content user index. 10. The method of claim 9, wherein the first latent factor model is of the form: Zi,j=(WC)i,j Yi,j˜Ber(Φ(Zi,j)), wherein Ber(z) represents the Bernoulli distribution with success probability z, wherein Φ(z) denotes an inverse link function that maps a real value z to the success probability of a binary random variable. 11. The method of claim 1, wherein the universe of possible values is an ordered set of P labels, wherein P is greater than or equal to two, wherein the first latent factor model characterizes a statistical relationship between (WC)i,j and a corresponding preference value Yi,j of the set of preference values, wherein (WC)i,j denotes the (i,j)th entry of the product matrix WC, wherein W is the association matrix, wherein C is the content-preference matrix, wherein i is a content item index, wherein j is a content user index. 12. The method of claim 11, wherein P is greater than two. 13. The method of claim 11, wherein the first latent factor model is of the form: Zi,j(WC)i,j Yi,j=g(Zi,j+εi,j), wherein Zi,j represents an ideal real-valued preference value corresponding to the jth content user for the ith content item, wherein εi,j represents random measurement noise or uncertainty in measurement, wherein g is a quantizer function that maps from the real line into the set of labels. 14. The method of claim 1, further comprising: receiving additional input data that includes a collection of M tags and information specifying a Q×M matrix T, wherein Q is the number of the content items, wherein, for each of the content items, a corresponding subset of the M tags have been assigned to the content item, wherein for each content item, the matrix T identifies the corresponding subset of the M tags, wherein the association matrix W includes entries that represent the strength of association between each of the Q content items and each concept in the set of K concepts; computing an estimate of an M×K matrix A, wherein entries of the matrix A represent strength of association between each of the M tags and each of the K concepts. 15. The method of claim 14, further comprising: displaying a bipartite graph based on the estimated matrix A, wherein the bipartite graph includes tag nodes and concept nodes and links between at least a subset of the tag nodes and at least a subset of the concept nodes, wherein the tag nodes represent the M tags, wherein the concept nodes represent the K concepts. 16. The method of claim 1, wherein each of the preference values has been selected from an ordered set of P labels, wherein P is greater than or equal to two, wherein the input data also includes a collection of tags and a item-tag index set, wherein the item-tag index set indicates, for each of the content items, which of the tags have been assigned to that content item, wherein the first latent factor model characterizes a statistical relationship between entries (WC)i,j of the product matrix WC and corresponding preference values Yi,j of the set of preference values, wherein i is a content item index, wherein j is a content user index, wherein W is the association matrix, wherein C is the content-preference matrix. 17. The method of claim 1, wherein the input data also includes word frequency data, wherein each of the content items is associated with a corresponding set of text, wherein the word frequency data is related to a vocabulary of words that has been derived from a union of the text sets over the content items, wherein the word frequency data indicates the frequency of occurrence of each vocabulary word in the text set of each content item; wherein the output data also includes a word-concept matrix T comprising strengths of association between the vocabulary words and the K concepts, wherein said computing includes minimizing an objective with respect to the association matrix W, the content-preference matrix C and the word-concept matrix T, wherein the objective includes at least: a negative log likelihood of the response data parameterized at least by the association matrix and the content-preference matrix; a negative log likelihood of the word frequency data parameterized at least by the association matrix and the word-concept matrix T. storing the output data in a memory. 18. The method of claim 1, wherein the input data includes a word-frequency matrix B, wherein the universe of possible values is an ordered set of P labels, wherein P is greater than or equal to two, wherein each of the content items is associated with a corresponding set of text, wherein the matrix B is related to a vocabulary of words that has been derived from a union of the text sets taken over the content items, wherein the matrix B includes entries Bi,v that indicate the frequency of occurrence of each vocabulary word in the text set of each content item; wherein said computing the output data based on the input data uses a second latent factor model in addition to the first latent factor model, wherein the output data also includes a word-concept matrix T, wherein the matrix T includes entries Tk,v that represent a strength of association between each vocabulary word and each of the K concepts, wherein the first latent factor model characterizes a statistical relationship between entries (WC)i,j of the product matrix WC and corresponding preference values Yi,j of the set of preference values, wherein W is the association matrix, wherein C is the content-preference matrix, wherein i is a content item index, wherein j is a content user index, wherein the second latent factor model characterizes a statistical relationship between entries (WT)i,v of the product matrix WT and entries Bi,v of the matrix B. 19. The method of claim 1, wherein the input data also includes a second set of preference values that have been assigned to the content items, wherein the second set of preference values have been provided by a new content user, the method further comprising: performing a singular value decomposition on the content-preference matrix C to obtain a factorization of the form C=USVT, wherein U is an matrix whose columns are orthogonal, wherein S is an R×R diagonal matrix, wherein V is a matrix whose columns are orthogonal, wherein R=rank(C); computing a latent preference vector v* for the new content user by estimating a minimum of an objective function with respect to vector argument v, subject to one or more conditions including a norm constraint on the vector argument v, wherein entries of the latent preference vector v* represent the extent of the new content user's preference for each of R latent factors implicit in the content-preference matrix C. 20. The method of claim 19, wherein the objective function comprises ΣiεΩobs−log p(Yi*|wiTUSv) wherein Ωobs is an index set indicating which of the content items were rated by the new content user, wherein Yi* represents the preference value assigned to the ith content item by the new content user, wherein wiT represents the ith row of the matrix W. 21. A non-transitory memory medium storing program instructions, wherein the program instructions, when executed by a computer, cause the computer to implement: receiving input data that includes response data, wherein the response data includes a set of preference values that have been assigned to content items by content users, wherein the preference values are drawn from a universe of possible values; computing output data based on the input data using a first latent factor model, wherein the output data includes at least: an association matrix that defines a set of K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept; displaying a visual representation of at least a subset of the association strengths in the association matrix and/or at least a subset of the extents in the concept-preference matrix. 22. A system comprising: a processor; and memory storing program instructions, wherein the program instructions, when executed by the processor, cause the processor to implement: receiving input data that includes response data, wherein the response data includes a set of preference values that have been assigned to content items by content users, wherein the preference values are drawn from a universe of possible values; computing output data based on the input data using a first latent factor model, wherein the output data includes at least: an association matrix that defines a set of K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept; displaying a visual representation of at least a subset of the association strengths in the association matrix and/or at least a subset of the extents in the concept-preference matrix.