Group Recommendations in MapReduce

Vasilis Efthymiou (), Petros Zervoudakis (), Kostas Stefanidis () and Dimitris Plexousakis ()
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Vasilis Efthymiou: University of Crete
Petros Zervoudakis: University of Crete
Kostas Stefanidis: University of Tampere
Dimitris Plexousakis: ICS-FORTH

A chapter in Strategic Innovative Marketing, 2017, pp 495-500 from Springer

Abstract: Abstract Recommender systems have received significant attention, with most of the proposed methods focusing on recommendations for single users. However, there are contexts in which the items to be suggested are not intended for a user but for a group of people. For example, assume a group of friends or a family that is planning to watch a movie or visit a restaurant. In this paper, we propose an extensive model for group recommendations that exploits recommendations for items that similar users to the group members liked in the past. We follow two different approaches for offering recommendations to the members of a group: considering the members of a group as a single user, and recommending to this user items that similar users liked, or estimating first how much each group member would like an item, and then, recommend the items that would (dis)satisfy the most (least) members of the group. For each of the two approaches, we introduce a different MapReduce algorithm, and evaluate the results in real data from the movie industry.

Keywords: Recommender System; Single User; Query User; Relevance Score; Similar User (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-56288-9_66

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DOI: 10.1007/978-3-319-56288-9_66

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