MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings

Anders H. Brams, Anders L. Jakobsen, Theis E. Jendal, Matteo Lissandrini, Peter Dolog, Katja Hose

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)

Abstract

Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.

Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Number of pages8
PublisherAssociation for Computing Machinery
Publication date19 Oct 2020
Pages2975-2982
ISBN (Electronic)9781450368599
DOIs
Publication statusPublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/TerritoryIreland
CityVirtual, Online
Period19/10/202023/10/2020
SponsorACM SIGIR, ACM SIGWEB

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • collaborative filtering
  • content-based filtering
  • dataset
  • knowledge graph
  • recommender systems

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