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This paper presents an personalized recommendation model to recommend potentially interesting resources to users based on the users' search behaviors and resource properties. This model builds on the user-based collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: users' preference description, similar users' calculation and the resource recommending...
In this paper, we focus on Collaborative Filtering to provide recommendations to users that fit their profiles. We employed two methods: (1) K-Nearest Neighbors classifier, and (2) a fast implementation of Collaborative Filtering approach: “user-to-user fast XOR bit operation”. Both techniques serve the same objective, which is modifying the user's ontology profile (semantic profile). Technically,...
Effective knowledge sharing and its fast spread play an important role in realizing knowledge management in an organization. The deficiency of existing knowledge sharing technological measures restrict the spread of knowledge and its efficiency, thus users have to spend large amount of time to search all kinds of knowledge they need. This study proposes a knowledge recommendation algorithm to improve...
Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous userspsila interactions. In this paper, we propose an algorithm to solve the Web page recommendation problem. In our algorithm,...
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