Exploring the potential of the resolving sets model for introducing serendipity to recommender systems

Noa Tuval

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the "beyondaccuracy" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graphtheory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.

Original languageEnglish
Title of host publicationACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages353-356
Number of pages4
ISBN (Electronic)9781450360210
DOIs
StatePublished - 7 Jun 2019
Event27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Publication series

NameACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Country/TerritoryCyprus
CityLarnaca
Period9/06/1912/06/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

  • Beyond accuracy metrics
  • Recommender systems
  • Resolving sets in a graph
  • Serendipity in recommendations
  • User modeling

ASJC Scopus subject areas

  • Software

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