Abstract:

Collaborative filtering （CF for short） technology， as an important component of recommender system， plays a key role in personalized advertising. On the other hand， although various CF models are aimed at capturing personalized preferences of individuals， their training process are without regarding differences among the individual， where given a training algorithm， the model is always trained with the same training data， no matter what the target object is. This approach， however， is too general to the individuals， for given a specific target user， in order to personalize the recommendations， it might be more reasonable to first personalize the training process. In this work， MORE， a collaborative filtering model with MinOrity REport is presented. MORE trains the CF model in an user specific mode. Given a target user， MORE essentially works in three steps： First， it constitutes a personalized expert set for the target user with the Elastic Net model； Second， it constructs a sub matrix of the origin “user-item” rating matrix， where all values are only from the target user and the expert users. Third， it performs matrix imputation on the newly constructed matrix， and obtain estimations for the target user’s uncollected rating values. MORE is evaluated with two different CF models on two real movie rating data sets， all results show that the proposed model is promising.

Key words: collaborative filtering, sparse selection, personalization, recommender system