Judging Similarity: A User-Centric Study of Related Item Recommendations

Yuan Yao, F Maxwell Harper

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Scopus citations

Abstract

Related item recommenders operate in the context of a particular item. For instance, a music system's page about the artist Radio-head might recommend other similar artists such as The Flaming Lips. Often central to these recommendations is the computation of similarity between pairs of items. Prior work has explored many algorithms and features that allow for the computation of similarity scores, but little work has evaluated these approaches from a user-centric perspective. In this work, we build and evaluate six similarity scoring algorithms that span a range of activity-and content-based approaches. We evaluate the performance of these algorithms using both ooine metrics and a new set of more than 22,000 user-contributed evaluations. We integrate these results with a survey of more than 700 participants concerning their expectations about item similarity and related item recommendations. We end that content-based algorithms outperform ratings-and clickstream-based algorithms in terms of how well they match user expectations for similarity and recommendation quality. Our results yield a number of implications to guide the construction of related item recommendation algorithms.
Original languageEnglish (US)
Title of host publicationProceedings of the 12th ACM Conference on Recommender Systems - RecSys '18
Pages288-296
Number of pages9
DOIs
StatePublished - 2018

Publication series

NameProceedings of the 12th ACM Conference on Recommender Systems - RecSys '18

Keywords

  • CCS CONCEPTS • Information systems → Recommender s
  • KEYWORDS recommender systems
  • Similarity measures
  • User studies
  • collaborative eltering
  • related item recommendations
  • rule mining
  • similarity metrics
  • use
  • word2vec
  • • Human-centered computing → Collaborative el-teri

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