Improving latent factor model based collaborative filtering via integrated folksonomy factors

Latent Factor Model (LFM) based approaches are becoming popular when implementing Collaborative Filtering (CF) recommenders, due to their high recommendation accuracy. However, current LFM approaches address the accuracy issue only based on the rating data, whereas early research indicates that integrating information from additional data sources is helpful to the recommendation accuracy. In this work we focus on improving the recommendation accuracy of a LFM based CF recommender by integrating folksonomy information. To implement this approach, we first propose a novel model named Item Folsonomy Relevance (IFR) to analyze the item relevance inside the folksonomy; we subsequently integrate the latent factors of the IFR model and rating data through probabilistic matrix factorization (PMF), a state-of-the-art matrix factorization technique, to produce recommendations based on information from both the ratings and folksonomy simultaneously. The experiments on MovieLens dataset showed that compared to two state-of-the-art LFM approaches and another folksonomy-augmented recommder, our approach could obtain advantage in recommendation accuracy.