Do users matter?

The contribution of user-driven feature weights to open dataset recommendations

Anusuriya Devaraju, Shlomo Berkovsky

Research output: Chapter in Book/Report/Conference proceedingConference abstract

Abstract

The vast volumes of open data pose a challenge for users in finding relevant datasets. To address this, we developed a hybrid dataset recommendation model that combines content-based similarity with item-to-item co-occurrence. The features used by the recommender include dataset properties and usage statistics. In this paper, we focus on fi ne-tuning the weights of these features. We experimentally compare two feature weighting approaches: a uniform one with predefined weights and a user-driven one, where the weights are informed by the opinions of system users. We evaluated the two approaches in a study, involving the users of a real-life data portal. The results suggest that user-driven feature weights can improve dataset recommendations, although not at all levels of data relevance, and highlight the importance of incorporating target users in the design of recommender systems.
Original languageEnglish
Title of host publicationProceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017)
EditorsDomonkos Tikk, Pearl Pu
PublisherCEUR Workshop Proceedings
Number of pages2
Publication statusPublished - 2017
Externally publishedYes
Event11th ACM Conference on Recommender Systems, RecSys '17 - Como, Italy
Duration: 27 Aug 201731 Aug 2017

Conference

Conference11th ACM Conference on Recommender Systems, RecSys '17
CountryItaly
CityComo
Period27/08/1731/08/17

Keywords

  • feature weighting
  • hybrid recommender system
  • open data

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