Towards Privacy Compliant and Anytime Recommender Systems

The original publication is available at www.springerlink.comInternational audienceRecommendation technologies have traditionally been used in domains such as E-commerce and Web navigation to recommend resources to customers so as to help them to get the pertinent resources. Among the possible approaches is collaborative filtering that does not take into account the content of the resources: only the traces of usage of the resources are considered. State of the art models, such as sequential association-rules and Markov models, that can be used in the frame of privacy concerns, are usually studied in terms of performance, state space complexity and time complexity. Many of them have a large time complexity and require a long time to compute recommendations. However, there are domains of application of the models where recommendations may be required quickly. This paper focuses on the study of how these state of the art models can be adapted so as to be anytime. In that case recommendations can be proposed to the user whatever is the computation time available, the quality of the recommendations increases according to the computation time. We show that such models can be adapted so as to be anytime and we propose several strategies to compute recommendations iteratively. We also show that the computation time needed by these new models is not increased compared to classical ones; even so, it sometimes decreases

User Semantic Preferences for Collaborative Recommendations

International audiencePersonalized recommender systems provide relevant items to users from huge catalogue. Collaborative filtering (CF) and content-based (CB) filter- ing are the most widely used techniques in personalized recommender systems. CF uses only the user-rating data to make predictions, while CB filtering relies on semantic information of items for recommendation. In this paper we present a new approach taking into account the semantic information of items in a CF system. Many works have addressed this problem by proposing hybrid solu- tions. In this paper, we present another hybridization technique that predicts us- ers "preferences for items based on their inferred preferences for semantic in- formation. With this aim, we propose a new approach to build user semantic profile to model users‟ preferences for semantic information of items. Then, we use this model in a user-based CF algorithm to calculate the similarity between users. We apply our approach to real data, the MoviesLens dataset, and com- pare our results to standards user-based and item-based CF algorithms

A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel

Abstract. The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively han-dle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed ker-nel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.

User Modeling Based on Emergent Domain Semantics

Abstract. In this paper we present an approach to user modeling based on the domain model that we generate automatically by resource (text) content processing and analysis of associated tags from a social annotation service. User’s interests are modeled by overlaying the domain model – via keywords extracted from resource’s (text) content, and tags assigned by the user or other (similar) users. The user model is derived automatically. We combine content- and tag-based approaches, shifting our approach beyond flat “folksonomical ” representation of user interest