CR-IIA: Collaborative Recommendation Algorithm Based on Implicit Item Associations

Recommendation system provides prediction of users' preference and guidance for users' purchase. Probabilistic matrix factorization (PMF) as a widely-used collaborative filtering approach so far, generally has excellent performance for personalised recommendation. Nevertheless, it still suffers from data sparsity problem. In this paper, we present a novel recommendation algorithm CR-IIA which combines matrix factorization and association rule mining. Our method employs ItemCorrelation to measure the latent correlation among items given by association rule mining. And ItemCorrelations are integrated into PMF model to optimize feature vectors of items. The factor of ItemCorrelations makes rating prediction more accurate and realistic. Compared with social trust model and context-based model, our approach improves the accuracy of ratings prediction without aggravating data sparsity problem caused by additional information. Moreover, our framework also allows additional dimensionality to be considered simultaneously, which benefits subsequent research in personalised recommendation based on matrix factorization. We conduct experiments on a real- world data set MovieLens, and the results show that our model outperforms the item-oriented approaches.