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1 Introduction Recommender Systems (RSs) are often assumed to present items to users for one reason – to recommend items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the " recommending " the system will do. There is another reason for presenting an item to the user: to(More)
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed(More)
The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly(More)
We present a prototype of a novel interactive food recom-mender for groups of users that supports groups in planning their meals through a conversational process based on cri-tiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and cri-tiquing, and a utility function incorporating healthiness and diet(More)
One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper(More)
Much of the variation in trace metal tissue concentrations in marine invertebrates has been attributed to the variety in individual organism size, age and sex. This study assessed the relationship between total mercury (Hg) concentrations in edible tissue, exoskeleton and viscera with length, weight and gender for 69 samples of crustaceans, Penaeus(More)
Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In(More)
The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item ratings. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, not all the ratings are equally useful and therefore, in order to(More)
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 of ratings for items. The accuracy of recommendations largely depends on the quality and quantity of the ratings (preferences) provided(More)