Informative household recommendation with feature-based matrix factorization

Abstract

In this paper, we describe our solutions to the first track of CAMRa2011 challenge. The goal of this track is to generate a movie ranking list for each household. To achieve this goal, we propose to use the ranking oriented matrix factorization and the matrix factorization with negative examples sampling. We also adopt feature-based matrix factorization framework to incorporate various contextual information to our model, including user-household relations, item neighborhood, user implicit feedback, etc.. Finally, we elaborate two kinds of methods to recommend movies for each household based on our models. Experimental results show that our proposed approaches achieve significant improvement over baseline methods.

A Comprehensive Collaborating Filtering Approach using Extended Matrix Factorization and Autoencoder in Recommender System

“…Combination of this two techniques have been implemented in this paper with good performances. So in this paper, the features for the users have been extracted by using the Autoencoder (AE) [8] and the Extended Matrix Factorization (EMF) [9]. After that, the similarity between the users have been calculated and by using the similarity, a recommendation prediction algorithm have also been proposed to accelerate the performances of the recommender system.…”