Transition regularized matrix factorization for sequential recommendation
Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related ite...
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| creator | Yang, Hao Samadi, Shamim Das, Mahashweta |
| description | Apparatuses, methods, and systems are provided for making sequential recommendations using transition regularized non-negative matrix factorization. A non-application specific collaborative filtering based personalized recommender system can recommend a next logical item from a series of related items to a user. The recommender system can recommend a next desirable or series of next desirable new items to the user based on the historical sequence of all user-item preferences and a user's most recent interaction with an item. An asymmetric item-to-item transition matrix can capture aggregate sequential user-item interactions to design a loss function for matrix factorization that incorporates the transition information during decomposition into low-rank factor matrices. |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Transition regularized matrix factorization for sequential recommendation |
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