Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction
LI Gai1, CHEN Qiang3, LI Lei2
1. Department of Electronic and Information Engineering, Shunde Polytechnic, Foshan, Guangdong 528300, China;
2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;
3. Department of Computer Science, Guangdong University of Education, Guangzhou, Guangdong 510303, China
Abstract：Collaborative filtering (CF) recommendation algorithm is widely used in the field of e-commerce.The previous researches on CF focused on either rating prediction or ranking prediction.In order to take into account these two aspects,a collaborative filtering recommendation algorithm based on rating prediction and ranking prediction (Unified Recommendation Algorithm,URA) is proposed.URA shares common latent features of users and items in PMF (Probabilistic Matrix Factorization,rating-oriented) and xCLiMF (Extended Collaborative Less-is-More Filtering,ranking-oriented) algorithms,and PMF learns improved latent features of users and items in URA,so that URA improves the performance of ranking recommendation.Experimental results showed that our proposed URA Algorithm outperformed PMF and xCLiMF algorithms over evaluation metrics NDCG and ERR,and that the complexity of URA is shown to be linear with the number of observed ratings.URA is suitable for big data processing in the field of internet information recommendation.
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2017, Vol. 45 
