A Lesson learned from PMF based approach for Semantic Recommender System

Abstract

Linked Open Data cloud is being conceived and published to improve the usability and performance of various applications including Recommender System. While most of the existing works incorporate semantic web information into recommendation system by exploiting content based method, we introduced a collaborative filtering based semantic dual probabilistic matrix factorization approach. We have used semantic item features, generated in an unsupervised manner and incorporated them into user-item preference matrix for recommendation by co-factoring two matrices. To mitigate the difficulty of high dimensionality, sparsity and possible noise in the semantic item-property matrix, Singular Value Decomposition was used as an unsupervised preprocessing step. To evaluate our new approach, RMSE are compared with 10 state-of-art algorithms especially Probabilistic Matrix Factorization which our method is based on, Precision is also compared between our method and PMF. Although similar dual matrix factorization approaches exist but most of them deal with very small item property matrix with abundant entries while our approach introduced a high dimensional and sparse item property matrix through an unsupervised automatic way which also alleviate the efforts to create those item property matrices.