篇名 | An Effective Content-based Recommendation Method forWeb Browsing Based on Keyword Context Matching |
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卷期 | 1:2 |
作者 | Feng-Hsu Wang 、 Shih-Yao Jian |
頁次 | 049-059 |
關鍵字 | Web browsing 、 recommender system 、 content-based recommendation 、 association mining 、 keyword context |
出刊日期 | 200610 |
This paper presents a novel content-based recommendation method which recommends web pages resembling to a user’s recent interests in a web site. Traditionally, a web page is recommended based on a comparison between a user’s profile and web contents that are represented as a set of feature keywords. This paper proposes a new approach to representing and extracting page features called keyword contexts. A keyword context is a set of discriminating words that occur together often in the web pages, which could capture more semantic information than a single keyword. The keyword contexts are extracted by analyzing Web pages with a new feature extraction method that combines the IR (Information Retrieval) and association mining techniques. Three methods for establishing a user’s interest profile based on keyword contexts of recently-visited web pages are proposed and compared. Web pages most similar to the user’s interest profile in terms of keyword contexts are then recommended. Finally, an application of this recommendation mechanism to an e-learning web site is presented. The experimental results showed that the method is better than the pure-keyword approach.