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- W29157085 abstract "People spend far more time searching information over the Internet than using it, because the desired information is often buried within a long list of searched results. Personalized internet access is a feasible solution to solve this search vs. use dilemma, which helps identify the web documents users truly need. A user’s interests are usually represented by a profile. In this research, an improved vector space model representation is proposed to improve the user interests management efficiency. Based on this, the research further proposes a method for user multiinterest modeling integrated with semantic similar network (SSN). It studies the feature selection in user modeling, and proposes a feature selection method combining TF and TF-IDF that is proved a better performance in the test. Finally a complete module design is presented, which provides a personalized recommendation system for practical applications." @default.
- W29157085 created "2016-06-24" @default.
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- W29157085 date "2009-01-01" @default.
- W29157085 modified "2023-09-23" @default.
- W29157085 title "USER MULTI-INTEREST MODELING BASED ON SEMANTIC SIMILAR NETWORK IN PERSONALIZED INFORMATION RETRIEVAL" @default.
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- W29157085 doi "https://doi.org/10.48009/2_iis_2009_257-266" @default.
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