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Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typicall ...
Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interacti ...
This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users’ point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combin ...
In this paper we propose a framework that characterizes user interaction with webGIS. Furthermore we present a technique to remotely test and evaluate such systems. Our framework does not only include parameters describing the usability of a system, but re ...
This paper discusses the 3A recommender system that targets CSCL (computer-supported collaborative learning) and CSCW (computer-supported collaborative work) environments. The proposed system models user interactions in a heterogeneous graph. Then, it appl ...
Group recommender systems suggest items of interest to a group of people. Traditionally, group recommenders provide recommendations by aggregation the group membersâ preferences. Nowadays, there is a trend of decentralized group recommendation process th ...
We propose a method to study and characterize the spatial and temporal properties of degenerate photon pairs emitted in SPDC, using a filtering system combined with temperature variation of the nonlinear crystal. The photons can be distinguished. We relate ...
The advent of online social networks created new prediction opportunities for recommender systems: instead of relying on past rating history through the use of collaborative filtering (CF), they can leverage the social relations among users as a predictor ...
Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...
User profiling is a useful primitive for constructing personalised services, such as content recommendation. In the present paper we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local informatio ...
