Graph Filtering for Recommendation on Heterogeneous Information Networks

Abstract

Various kinds of auxiliary data in web services have been proved to be valuable to handler data sparsity and cold-start problems of recommendation. However, it is challenging to develop effective approaches to model and utilize these various and complex information. Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to model auxiliary data for sparsity recommendation, named HIN based recommendation. But most of these HIN based methods rely on meta path-based similarity or graph embedding, which cannot fully mine global structure and semantic features of users and items. Besides, these methods, utilizing extended matrix factorization model or deep learning model, suffer expensive model-building problem and cannot treat personal latent factors carefully since their global objective functions. In this paper, we model both rate and auxiliary data through a unified graph and propose a graph filtering (GF) recommendation method on HINs. Distinct from traditional HIN based methods, GF uses a rate pair structure to represent user's feedback information and predict the rating that says, 'a predicted rating depends on its similar rating pairs.' Concretely, we design a semantic and sign value-aware similarity measure based on SimRank, named Constrained SimRank, to weight rating pair similarities on the unified graph and compute the predicting rate score for an active user via weighted average of all similar ratings. Various semantics behind edges of the unified graph have different contributions for the prediction. Thus, an adaptive framework is proposed to learn the weights of different semantic edges and products an optimized predicted rating. Finally, experimental studies on various real-world datasets demonstrate that GF is effective to handler the sparsity issue of recommendation and outperforms the state-of-the-art techniques.