Movie recommender system for profit maximization

Amos Azaria, Avinatan Hassidim, Sarit Kraus, Adi Eshkol, Ofer Weintraub, Irit Netanely

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

75 Scopus citations

Abstract

Traditional recommender systems minimize prediction error with respect to users' choices. Recent studies have shown that recommender systems have a positive effect on the provider's revenue. In this paper we show that by providing a set of recommendations different than the one perceived best according to user acceptance rate, the recommendation system can further increase the business' utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content. We performed a large body of experiments comparing a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible reduce in satisfaction by providing the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Differences in user satisfaction between the lists is negligible, and not statistically significant. We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.

Original languageEnglish
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages121-128
Number of pages8
DOIs
StatePublished - 2013
Externally publishedYes
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: 12 Oct 201316 Oct 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period12/10/1316/10/13

Keywords

  • Human modeling
  • Movies
  • Recommender systems

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