Media content is recommended based on suitability for a designated activity. A vector engine is trained using a plurality of lists, each of the lists containing metadata associated with a plurality of media objects. The vector engine includes a neural network trained with corpus data including (i) the plurality of lists (ii) a plurality of titles, each one of the titles associated with one of the lists, and (iii) the metadata associated with the plurality of media objects. Training the vector engine involves initializing, using the vector engine, a plurality of feature vectors representing each of the lists, each of the media objects, and each of a plurality of words in the titles of the lists. The training then further involves nudging, using the vector engine, the feature vectors based on a plurality of co-occurrences of the lists, the media objects, the words in the titles of the lists, or a combination thereof. A feature vector corresponding to an activity is identified among the feature vectors. At least one of the media objects, (ii) at least one of lists or (iii) a combination thereof suitable for the activity is selected based on cosine similarities between the feature vector corresponding to the activity and others of the feature vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-30. (canceled) 31. A method of determining an activity, comprising the steps of: training a vector engine using a plurality of lists, each list identifying a plurality of media objects, the vector engine including a neural network trained with corpus data that includes one or more of (i) the plurality of lists, (ii) a plurality of titles, each title associated with a respective one of the plurality of lists, (iii) a plurality of metadata, each metadata associated with the plurality of media objects identified in a respective one of the plurality of lists, and (iv) a plurality of interaction histories, wherein training the vector engine includes: initializing, using the vector engine, a plurality of feature vectors representing each of the plurality of media objects and each of the one or more of the plurality of lists, the plurality of titles, and the plurality of interaction histories, nudging, using the vector engine, the plurality of feature vectors based on a plurality of co-occurrences of (i) the plurality of lists, (ii) the plurality of media objects, (iii) the words in the plurality of titles, (iv) the plurality of metadata, or (v) a combination thereof, and associating selected feature vectors of the plurality of feature vectors with user activities; and determining the activity based on similarities between (i) at least one feature vector, of the plurality of feature vectors, corresponding to interaction data collected by the client device and (ii) feature vectors, of the plurality of feature vectors, which are associated with a user activity. 32. The method according to claim 31, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a title of a respective list. 33. The method according to claim 31, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list. 34. The method according to claim 33, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list in the form of a word appearing in a tag of the respective list. 35. The method according to claim 31, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a respective list. 36. The method according to claim 31, wherein the determining of the activity is based on cosine similarities. 37. The method according to claim 31, wherein the determining the activity based on similarities includes computing probabilities of similarity corresponding to respective activities. 38. The method according to claim 37, wherein the determining the activity based on similarities further includes selecting the activity with the greatest computed probability of similarity. 39. The method according to claim 31, wherein the neural network is comprised of at least one of a gradient descent model, a stochastic gradient descent model, a Continuous Bag-of-Words (CBOW) model, a skip-gram model, a hierarchical soft-max model and a negative sampling model. 40. The method according to claim 31, wherein the feature vectors associated with a user activity include a feature vector associated with at least one of running, exercise, or a workout. 41. A system comprising a non-transitory memory which stores computer-executable process steps and a processing device coupled to the memory and constructed to execute the process steps stored in the memory, wherein the stored process steps include steps which when executed by the processor cause the processor to perform the steps of: training a vector engine using a plurality of lists, each list identifying a plurality of media objects, the vector engine including a neural network trained with corpus data that includes one or more of (i) the plurality of lists, (ii) a plurality of titles, each title associated with a respective one of the plurality of lists, (iii) a plurality of metadata, each metadata associated with the plurality of media objects identified in a respective one of the plurality of lists, and (iv) a plurality of interaction histories, wherein training the vector engine includes: initializing, using the vector engine, a plurality of feature vectors representing each of the plurality of media objects and each of the one or more of the plurality of lists, the plurality of titles, and the plurality of interaction histories, nudging, using the vector engine, the plurality of feature vectors based on a plurality of co-occurrences of (i) the plurality of lists, (ii) the plurality of media objects, (iii) the words in the plurality of titles, (iv) the plurality of metadata, or (v) a combination thereof, and associating selected feature vectors of the plurality of feature vectors with user activities; and determining an activity based on similarities between (i) at least one feature vector, of the plurality of feature vectors, corresponding to interaction data collected by the client device and (ii) feature vectors, of the plurality of feature vectors, which are associated with a user activity. 42. The system according to claim 41, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a title of a respective list. 43. The system according to claim 41, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list. 44. The system according to claim 43, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list in the form of a word appearing in a tag of the respective list. 45. The system according to claim 41, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a respective list. 46. The system according to claim 41, wherein the determining of the activity is based on cosine similarities. 47. The system according to claim 41, wherein the determining the activity based on similarities includes computing probabilities of similarity corresponding to respective activities. 48. The system according to claim 47, wherein the determining the activity based on similarities further includes selecting the activity with the greatest computed probability of similarity. 49. The system according to claim 41, wherein the neural network is comprised of at least one of a gradient descent model, a stochastic gradient descent model, a Continuous Bag-of-Words (CBOW) model, a skip-gram model, a hierarchical soft-max model and a negative sampling model. 50. The system according to claim 41, wherein the feature vectors associated with a user activity include a feature vector associated with at least one of running, exercise, or a workout. 51. A non-transitory computer-readable storage medium storing a program for causing a computer to implement a method of determining an activity, the method comprising the steps of: training a vector engine using a plurality of lists, each list identifying a plurality of media objects, the vector engine including a neural network trained with corpus data that includes one or more of (i) the plurality of lists, (ii) a plurality of titles, each title associated with a respective one of the plurality of lists, (iii) a plurality of metadata, each metadata associated with the plurality of media objects identified in a respective one of the plurality of lists, and (iv) a plurality of interaction histories, wherein training the vector engine includes: initializing, using the vector engine, a plurality of feature vectors representing each of the plurality of media objects and each of the one or more of the plurality of lists, the plurality of titles, and the plurality of interaction histories, nudging, using the vector engine, the plurality of feature vectors based on a plurality of co-occurrences of (i) the plurality of lists, (ii) the plurality of media objects, (iii) the words in the plurality of titles, (iv) the plurality of metadata, or (v) a combination thereof, and associating selected feature vectors of the plurality of feature vectors with user activities; and determining the activity based on similarities between (i) at least one feature vector, of the plurality of feature vectors, corresponding to interaction data collected by the client device and (ii) feature vectors, of the plurality of feature vectors, which are associated with a user activity. 52. The non-transitory computer-readable storage medium according to claim 51, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a title of a respective list. 53. The non-transitory computer-readable storage medium according to claim 51, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list. 54. The non-transitory computer-readable storage medium according to claim 53, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to metadata associated with a respective list in the form of a word appearing in a tag of the respective list. 55. The non-transitory computer-readable storage medium according to claim 51, wherein the at least one feature vector corresponding to interaction data collected by the client device further corresponds to a respective list. 56. The non-transitory computer-readable storage medium according to claim 51, wherein the determining of the activity is based on cosine similarities. 57. The non-transitory computer-readable storage medium according to claim 51, wherein the determining the activity based on similarities includes computing probabilities of similarity corresponding to respective activities. 58. The non-transitory computer-readable storage medium according to claim 57, wherein the determining the activity based on similarities further includes selecting the activity with the greatest computed probability of similarity. 59. The non-transitory computer-readable storage medium according to claim 51, wherein the neural network is comprised of at least one of a gradient descent model, a stochastic gradient descent model, a Continuous Bag-of-Words (CBOW) model, a skip-gram model, a hierarchical soft-max model and a negative sampling model. 60. The non-transitory computer-readable storage medium according to claim 51, wherein the feature vectors associated with a user activity include a feature vector associated with at least one of running, exercise, or a workout.