Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Group Recommendation System for Facebook
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
Local Community Identification in Social Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
Personality aware recommendations to groups
Proceedings of the third ACM conference on Recommender systems
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
The adaptive web
The community-search problem and how to plan a successful cocktail party
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
A group recommendation system for online communities
International Journal of Information Management: The Journal for Information Professionals
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Recommender systems actively provide users with suggestions of potentially relevant items. In this paper we introduce double-sided recommendations, i.e., recommendations consisting of an item and a group of people with whom such an item could be consumed. We identify four specific instances of the double-sided recommendation problem and propose a general method for solving each of them (social comparison-based, group-priority, item-priority and samepriority methods), thus defining a framework for generating double-sided recommendations. We present the experimental evaluation we carried out, focusing on the restaurant domain as a use case, with the twofold aim of 1) assessing user liking for double-sided recommendations and 2) comparing the four proposed methods, testing our hypothesis that their perceived usefulness varies according to the specific problem instance users are facing. Our results show that users appreciate double-sided recommendations and that all four methods -and, in particular, the group-priority one- can generate useful suggestions.