Abstract
Due to the flexibility in modelling data heterogeneity, heterogeneous
information network (HIN) has been adopted to characterize complex and
heterogeneous auxiliary data in recommender systems, called HIN based
recommendation. It is challenging to develop effective methods for HIN based
recommendation in both extraction and exploitation of the information from
HINs. Most of HIN based recommendation methods rely on path based similarity,
which cannot fully mine latent structure features of users and items. In this
paper, we propose a novel heterogeneous network embedding based approach for
HIN based recommendation, called HERec. To embed HINs, we design a meta-path
based random walk strategy to generate meaningful node sequences for network
embedding. The learned node embeddings are first transformed by a set of fusion
functions, and subsequently integrated into an extended matrix factorization
(MF) model. The extended MF model together with fusion functions are jointly
optimized for the rating prediction task. Extensive experiments on three
real-world datasets demonstrate the effectiveness of the HERec model. Moreover,
we show the capability of the HERec model for the cold-start problem, and
reveal that the transformed embedding information from HINs can improve the
recommendation performance.
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