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Pros are continuously offered with a variety of info resources developing the necessity to make certain their relevance in the large quantity of accessible details. Collaborative and Social info Retrieval and entry: innovations for superior consumer Modeling provides present state of the art advancements together with case reports, demanding situations, and tendencies. protecting themes resembling recommender structures, person profiles, and collaborative filtering, this ebook informs and educates academicians, researchers, and box practitioners at the most recent developments in details retrieval.
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Extra info for Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling
The problem of data sparsity reveals itself in this example; not all users have rated all the content. It also paves the way for the algorithms we describe in the following sections, which aim at predicting ratings for each user. It is important to note, however, that the techniques described here can be equally applied to both user profiles, which contain a vector of content (or item) ratings, and item profiles, which contain a vector of user ratings (Sarwar et al, 2001; Linden et al, 2003). For example, a user-centered approach would refer to “Alice’s” profile as containing “Citizen Kane” and “Hannibal,” with 4 and 3 star ratings, respectively.
Margaritis, K. G. (2004). Enhancing collaborative filtering with demographic data: The case of item-based filtering. In 4th International Conference on Intelligent Systems Design and Applications (pp. 361–366). , de Vries, A. , & Reinders, M. J. (2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In 29th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 501-508). , & Minka, T. (2002). Novelty and redundancy detection in adaptive filtering.
Grouplens: An open architecture for collaborative filtering of netnews. In Conference on Computer Supported Cooperative Work (pp. 175–186). ACM. Sarwar, B. , Konstan, J. , & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In ACM Conference on Electronic Commerce (pp. 158–167). Sarwar, B. , & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In 10th International World Wide Web Conference. , & Maes, P. (1995). Social information filtering: Algorithms for automating “word of mouth”.