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Erschienen in:
Sentence Compression with Reinforcement Learning Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Semantic Path-Based Similarity Measure for Weighted Heterogeneous Information Networks

verfasst von : Chunxue Yang, Chenfei Zhao, Hengliang Wang, Riming Qiu, Yuan Li, Kedian Mu

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Social Bayesian Personal Ranking for Missing Data in Implicit Feedback Recommendation
Nächstes Kapitel Cross-Domain Recommendation for Mapping Sentiment Review Pattern
Fußnoten
1
Yelp is a website which publishes crowd-sourced reviews about local businesses. https://​www.​yelp.​com/​.
 
4
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.
 
Literatur
1.
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Lao, N., Cohen, W.W.: Fast query execution for retrieval models based on path-constrained random walks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 881–888. ACM (2010)
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Shi, C., Kong, X., Huang, Y., Yu, P.S., Wu, B.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)CrossRef
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Shi, C., Yu, P.S.: Heterogeneous Information Network Analysis and Applications. DA. Springer, Cham (2017)CrossRef
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Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pp. 453–462. ACM (2015)
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Shi, C., Zhou, C., Kong, X., Yu, P.S., Liu, G., Wang, B.: HeteRecom: a semantic-based recommendation system in heterogeneous networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1552–1555. ACM (2012)
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Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. Newsl. 14(2), 20–28 (2013)CrossRef
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Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4, 992–1003 (2011)
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Yu, X., et al.: Recommendation in heterogeneous information networks with implicit user feedback. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 347–350. ACM (2013)
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Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 635–644. ACM (2017)
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Zheng, J., Liu, J., Shi, C., Zhuang, F., Li, J., Wu, B.: Recommendation in heterogeneous information network via dual similarity regularization. Int. J. Data Sci. Anal. 3, 35–48 (2017)CrossRef
Metadaten
Titel
A Semantic Path-Based Similarity Measure for Weighted Heterogeneous Information Networks
verfasst von
Chunxue Yang
Chenfei Zhao
Hengliang Wang
Riming Qiu
Yuan Li
Kedian Mu
Copyright-Jahr
2018
Buch
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-99364-5

Electronic ISBN: 978-3-319-99365-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99365-2_28