Enhancing Collaborative Filtering Using Implicit Relations in Data

International audienceThis work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Fac-torization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into " semantic values " , where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be par-allelized, alleviating time processing in large amount of data

Effective decision support for semantic web service selection

The objective of this dissertation is to demonstrate the feasibility of the vision of the Internet of Services based on Semantic Web Services by suggesting an approach to end-user mediated Semantic Web Service selection. Our main contribution is an incremental and interactive approach to requirements elicitation and service selection that is inspired by example critiquing recommender systems. It alternates phases of intermediate service recommendation and phases of informal requirements specification. During that process, the user incrementally develops his service requirements and preferences and finally makes a selection decision. We demonstrate how the requirements elicitation and service selection process can be directed and focused to effectively reduce the system's uncertainty about the user's service requirements and thus to contribute to the efficiency of the service selection process. To acquire information about the actual performance of available services and thus about the risk that is associated with their execution, we propose a flexible feedback system, that leverages reported consumer experiences made in past service interactions. In particular, we provide means that allow to detailedly describe a service's performance with respect to its multiple facets. This is supplemented by a user-adaptive method that effectively assists service consumers in providing such feedback as well as a privacy-preserving technique for feedback propagation. We also demonstrate that available consumer feedback can be effectively exploited to assess the degree and kind of risk that is associated with the execution of an offered service and show how the user can be effectively made aware of this risk. In contrast to many other approaches related to Semantic Web Service technology, we performed an extensive and thorough evaluation of our contribution and documented its results. These show the effectiveness and efficiency of our approach