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Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

Published: 04 August 2017 Publication History

Abstract

Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.

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MP4 File (zhao_recommendation_fusion.mp4)

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cover image ACM Conferences
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2017
2240 pages
ISBN:9781450348874
DOI:10.1145/3097983
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 August 2017

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Author Tags

  1. collaborative filtering
  2. factorization machine
  3. heterogeneous information networks
  4. recommendation system

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2025)Knowledge-driven hierarchical intents modeling for recommendationExpert Systems with Applications10.1016/j.eswa.2024.125361259(125361)Online publication date: Jan-2025
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