Combining Usage, Content, and Structure Data to Improve Web Site Recommendation
Combining Usage, Content, and Structure Data to Improve Web Site Recommendation
复制标题
结合使用情况、内容和结构数据来改进网站推荐
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Osmar R Zaiane
中科院分区：
文献类型：
--
作者：
Jia Li;Osmar R Zaiane
Web推荐系统预测Web用户的需求，并为他们提供个性化导航的建议。人们曾期望这类系统有光明的前途，特别是在电子商务和电子学习环境中。然而，尽管它们已经在Web挖掘和机器学习领域进行了深入的探索，并且已经出现了一些商业化的系统，但这些系统的推荐质量和用户满意度仍然不是最佳的。在本文中，我们研究了一种新的Web推荐系统，它结合了使用数据，内容数据和结构数据在一个网站，以产生用户导航模型。然后这些模型被反馈到系统中，以推荐用户快捷方式或页面资源。我们还提出了一个评价机制来衡量推荐系统的质量。初步实验表明，该系统可以显着提高网站推荐的质量。
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.