Learning Preference Models in Recommender Systems

Gemmis, Marco de; Iaquinta, Leo; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Semeraro, Giovanni

doi:10.1007/978-3-642-14125-6_18

Cited by 63 publications

(53 citation statements)

References 30 publications

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“…Similarly, in [27], research on how recommender systems learn the user preferences (feedback gathering) and how they process them is presented. Examples of techniques for learning user profiles include the naive Bayes algorithm and the Rocchio method; the latter is also the basis for our recommendations.…”

Section: Non-invasive Recommender Systemsmentioning

confidence: 99%

A Privacy-by-Design Contextual Suggestion System for Tourism

Efraimidis

Drosatos

Arampatzis

et al. 2016

JSAN

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Abstract:We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive.

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Section: Non-invasive Recommender Systemsmentioning

confidence: 99%

A Privacy-by-Design Contextual Suggestion System for Tourism

Efraimidis

Drosatos

Arampatzis

et al. 2016

JSAN

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“…Recommender systems have significantly increased in the past decade. Preference learning in a recommender system is considered one of the most popular and significant techniques from Information Filtering (Eaton & Wagstaff, 2006;Gemmis, Iaquinta, Lops, Musto, Narducci, & Semeraro, 2009). Information filtering assists in the removal of insignificant information and content that does not need to be stored in a customer profile.…”

Section: Introductionmentioning

confidence: 99%

“…Information filtering assists in the removal of insignificant information and content that does not need to be stored in a customer profile. When a recommender system is applied, for instance, to learn the interests of users (Eaton & Wagstaff, 2006;Gemmis et al, 2009), it will study and learn some of the user's behavioural aspects in order to generate and recommend a list of products (Eaton & Wagstaff, 2006;Gemmis et al, 2009). Learning the user's preferences is one technique to discover the best outcomes to recommend items (Eaton & Wagstaff, 2006;Gemmis et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Data Mining Techniques and Preference Learning in Recommender Systems

Mohammed

Mouhoub

Alanazi³

et al. 2013

CIS

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The importance of implementing recommender systems has significantly increased during the last decade. The majority of available recommender systems do not offer clients the ability to make selections based on their choices or desires. This has motivated the development of a web based recommender system in order to recommend products to users and customers. The new system is an extension of an online application previously developed for online shopping under constraints and preferences. In this work, the system is enhanced by introducing a learning component to learn user preferences and suggests products based on them. More precisely, the new component learns from other customers' preferences and makes a set of recommendations using data mining techiques including classification, association rules and cluster analysis techniques. The results of experimental tests, conducted to evaluate the performance of this component when compiling a list of recommendations, are very promising.

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“…Two aspects are of interest when performing prediction: (i) the ratings of items the user has already evaluated if it's a content-based system or, (ii) the similar users'ratings while working with a collaborative filtering [5] [16]. The later, is one of the most frequently used techniques in recommender systems; it's based on the assumption that similar users with similar opinions may rate items similarly and so can help each other to find out useful information [17].…”

Section: Introductionmentioning

confidence: 99%

Towards recommender systems based on a fuzzy preference aggregation

Boulkrinat¹,

Hadjali²,

Mokhtari³

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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An approach to deal with user preference relations, instead of absolute ratings, in recommender systems is discussed. User's preferences are then ratings expressed qualitatively by using linguistic terms. This is a suitable technique when preferences are imprecise and vague. On the other hand, to avoid that the overall item rating may hide the users' preferences heterogeneity and mislead the system when predicting the items that users are interested in, multi-criteria ratings are used as well. User's items ratings are represented through a preference graph which highlight better items relationships. Similarity between users is performed on the basis of the similarity of their preference relations which can better capture similar users' ratings patterns. Some preliminary results shows that, our approach enhances the classical recommender system precision thanks to the graphs used for prediction which are more informative and reflect user's initial ratings relations in a better way.

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Learning Preference Models in Recommender Systems

Cited by 63 publications

References 30 publications

A Privacy-by-Design Contextual Suggestion System for Tourism

A Privacy-by-Design Contextual Suggestion System for Tourism

Data Mining Techniques and Preference Learning in Recommender Systems

Towards recommender systems based on a fuzzy preference aggregation

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