Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Asymmetric Information Model of State Dependence
Marketing Science
An MDP-Based Recommender System
The Journal of Machine Learning Research
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Recommender Systems Handbook
Empirical Analysis of the Impact of Recommender Systems on Sales
Journal of Management Information Systems
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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.