Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay

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Title: Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay
Authors: Smyth, Barry
Rafter, Rachael
Banks, Sam
Permanent link: http://hdl.handle.net/10197/8154
Date: 17-Jul-2016
Abstract: Recommender systems have become a familiar part of our online experiences, suggesting movies to watch, music to listen to, and books to read, among other things. To make relevant suggestions, recommender systems need an accurate picture of our preferences and interests and sometimes even our friends and influencers. This information can be difficult to come by and expensive to source. In this paper we describe a game-with-a-purpose designed to infer useful recommendation data as a side-effect of gameplay. The game is a simple, single-player matching game in which players attempt to match movies with their friends. It has been developed as a Facebook app and harnesses the social graph and likes of players as a source of game data. We describe the basic game mechanics and evaluate the utility of the recommendation knowledge that can be inferred from its gameplay as part of a live-user trial.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2016 ACM
Keywords: Recommender systemsGames-with-a-purpose
DOI: 10.1145/2930238.2930260
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization UMAP '16
Conference Details: 24th Conference on User Modeling Adaptation and Personalization (UMAP), Halifax, Canada, 13-16 July 2016
Appears in Collections:Insight Research Collection

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