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Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of...
E-learning systems have made considerable progress within the last few years. Nonetheless, the issue of learner privacy has been practically ignored. Existing E-learning standards offer some provisions for privacy and the security of E-learning systems offers some privacy protection, but remains unsatisfactory on several levels. On the other hand, privacy preserving solutions that are appropriate...
The rapid evolution of our world means that learning and knowledge sharing are fast becoming a key challenge for individuals and organizations. In this paper, we present a system called HELP, whose aim is to locate information and recommend experts in organizations. Each user is being viewed simultaneously as an expert and a learner. We use two approaches: The first one consists of making the system...
In the context of electronic commerce, recommender systems enable merchants to assist customers in finding available products that will best satisfy their need. However, a recommender system usually operates as a kind of black box from which customers receive recommendations for products. Of particular interest are recommender systems based on collaborative filtering, in which customers provide the...
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