HCoM: Item-Based Similarity Model for Heterogeneous Implicit Feedback

Lini Chen, Xin Xin, Dong Wong, Yue Ding

Abstract: Popularization of mobile devices promotes the development of E-commerce in which users produce plenty of implicit feedbacks (e.g., click, adding to wish list, purchase). Generally, the implicit feedback can be divided into the certain implicit feedback like purchase and the uncertain implicit feedback like click. However, certain implicit feedback is sparse while uncertain implicit feedback like click is more common. In recommender systems, most conventional methods only use certain implicit feedback, suffering from the sparsity problem. In this paper, we propose an improved item-based similarity model named HCoM(Heterogeneous-COnstraint Model) which utilizes heterogeneous implicit feedbacks to handle the sparsity problem of certain implicit feedback. In our model, the item similarity is learned using a structural equation modeling approach with a heterogeneous-constraint(HC) involved in regularization terms. As a result, the sparsity problem is alleviated and a higher accuracy is obtained. We conduct a set of experiments on authentic users-commodities mobile behavior datasets with different scales and sparsities to compare HCoM with state-of-the-art top-N recommendation methods. Experimental results show that HCoM achieves remarkable improvements in recommendation quality versus all methods compared.