Abstract
In recent years, recommender systems based on heterogeneous information networks (HIN) have gained wide attention. In order to generate more attractive recommendations, weighted heterogeneous information network (WHIN) has been proposed, which attaches attribute values to links. The widely-used similarity measures for HIN may fail to capture the semantics of weighted meta-path. This makes designing a similarity measure specially for WHIN more necessary. In this paper, we propose a semantic path-based similarity measure called WgtSim, which is a generalization of PathSim presented by Sun et al. Furthermore, to demonstrate the capability of WgtSim in capturing semantics, we apply WgtSim to recommender system on WHIN to predict ratings given by users. The experiments on two real datasets show that the recommender system with WgtSim outperforms that with previous measures.
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Notes
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Yelp is a website which publishes crowd-sourced reviews about local businesses. https://www.yelp.com/.
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Since the Yelp dataset in the CIKM paper [7] has not been published, we use another Yelp dataset in our experiments, which has sparer ratings than CIKM-Yelp (The density of rating matrix in CIKM-Yelp is reported in [12]). Thus the performance of Constrained PathSim is different from what they reported in their paper.
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Acknowledgements
This work was partly supported by the National Natural Science Foundation of China under Grant No. 61572002, No. 61170300, No. 61690201, and No. 61732001.
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Yang, C., Zhao, C., Wang, H., Qiu, R., Li, Y., Mu, K. (2018). A Semantic Path-Based Similarity Measure for Weighted Heterogeneous Information Networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_28
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