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- W2003790488 abstract "A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance." @default.
- W2003790488 created "2016-06-24" @default.
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- W2003790488 date "2009-06-15" @default.
- W2003790488 modified "2023-09-26" @default.
- W2003790488 title "CARES" @default.
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- W2003790488 doi "https://doi.org/10.1145/1555400.1555432" @default.
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