Translated Title

Recommender algorithm based on matrix factorization with multiple regularization parameters

Translated Abstract

Regularization　technique　is　needed　for　the　collaborative　filtering　recommendation　algorithm　based　on　the　gradi-ent　descent　matrix　factorization　to　restrict　the　problems.　Regularization　parameter　used　in　the　loss　function　can　control　the　trade-off　between　prediction　accuracy　of　the　model　and　overfitting　avoidance　to　the　training.　A　method　of　multiple　regular-ization　parameters　is　proposed.　It　can　obtain　the　regularization　parameters　according　to　the　user　activity　and　item　populari-ty,　avoid　the　overfitting　in　the　users　and　items　with　different　numbers,　and　get　better　prediction　accuracy.　The　experiment　results　show　that　this　method　is　correct　and　feasible.

Translated Keyword

collaborative filtering
probabilistic matrix factorization
recommender system
regularization parameter

Classification

TP311

Type

Project Name

Project No.

Access Number

WF:perioarticaljsjgcyyy201703014

Language

Chinese

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