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Multirelational Recommendation in Heterogeneous Networks

Published: 23 June 2017 Publication History

Abstract

Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for recommendation is essentially two dimensional, with the interaction between users and items characterized by a single relation. In many cases, such as social networks, users and items are joined in a complex web of relations, not readily reduced to a single value. Recent multirelational approaches to recommendation focus on the direct, proximal relations in which users and items may participate. Our approach uses the framework of complex heterogeneous networks to represent such recommendation problems. We propose the weighted hybrid of low-dimensional recommenders (WHyLDR) recommendation model, which uses extended relations, represented as constrained network paths, to effectively augment direct relations. This model incorporates influences from both distant and proximal connections in the network. The WHyLDR approach raises the problem of the unconstrained proliferation of components, built from ever-extended network paths. We show that although component utility is not strictly monotonic with path length, a measure based on information gain can effectively prune and optimize such hybrids.

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cover image ACM Transactions on the Web
ACM Transactions on the Web  Volume 11, Issue 3
August 2017
209 pages
ISSN:1559-1131
EISSN:1559-114X
DOI:10.1145/3113174
Issue’s Table of Contents
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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Publication History

Published: 23 June 2017
Accepted: 01 February 2017
Revised: 01 January 2017
Received: 01 April 2016
Published in TWEB Volume 11, Issue 3

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Author Tags

  1. Multi-relational recommender systems
  2. heteroegeous information networks
  3. hybrid recommender systems
  4. information gain
  5. meta-paths

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  • (2019)Extending a Tag-based Collaborative Recommender with Co-occurring Information InterestsProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320458(181-190)Online publication date: 7-Jun-2019
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