Options
Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay
Author(s)
Date Issued
2016-07-17
Date Available
2016-11-28T14:50:17Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2016 ACM
Language
English
Status of Item
Peer reviewed
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
This item is made available under a Creative Commons License
File(s)
Name
Harnessing Crowdsourced recommendatiion preference data from casual game play.pdf
Size
2.38 MB
Format
Owning collection
Scopus© citations
4
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Views
1215
Last Week
2
2
Last Month
4
4
Acquisition Date
Apr 17, 2024
Apr 17, 2024
Downloads
474
Last Week
3
3
Last Month
11
11
Acquisition Date
Apr 17, 2024
Apr 17, 2024