Abstract
Web recommender systems anticipate the needs of web users and provide them with recommendations to personalize their navigation. Such systems had been expected to have a bright future, especially in e-commerce and e-learning environments. However, although they have been intensively explored in the Web Mining and Machine Learning fields, and there have been some commercialized systems, the quality of the recommendation and the user satisfaction of such systems are still not optimal. In this paper, we investigate a novel web recommender system, which combines usage data, content data, and structure data in a web site to generate user navigational models. These models are then fed back into the system to recommend users shortcuts or page resources. We also propose an evaluation mechanism to measure the quality of recommender systems. Preliminary experiments show that our system can significantly improve the quality of web site recommendation.
Research funded in part by the Alberta Ingenuity Funds and NSERC Canada.
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Li, J., Zaïane, O.R. (2004). Combining Usage, Content, and Structure Data to Improve Web Site Recommendation. In: Bauknecht, K., Bichler, M., Pröll, B. (eds) E-Commerce and Web Technologies. EC-Web 2004. Lecture Notes in Computer Science, vol 3182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30077-9_31
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DOI: https://doi.org/10.1007/978-3-540-30077-9_31
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