Recommendations with optimal combination of feature-based and item-based preferences
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Many recommender systems rely on item ratings to predict users' preferences and generate recommendations. However, users often express preferences by referring to features of the items, e.g., "I like Tarantino's movies". But, it has been shown that user models based on feature preferences may lead to wrong recommendations. In this paper we cope with this issue and we introduce a novel prediction model that generate better item recommendations, especially in coldstart situations, by exploiting both item-based and featurebased preferences. We also show that it is possible to optimize the combination of the two types of preferences when actively requesting them to users.
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Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems Braunhofer, M; Elahi, M; Ge, M; Ricci, F (Springer, 2014)Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers with relevant recommendations, e. g., Places of Interest (POIs) to a tourist, based on user preference data, mainly in the form ...
Kalloori S; Ricci F; Tkalcic M (Association for Computing Machinery, Inc, 2016)Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building ...
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