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Finding User’s Interest Blocks using Significant Implicit Evidence for Web Browsing on Small Screen Devices

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Abstract

Recent researches on improving the efficiency and user experience of Web browsing on handhelds are seeking to solve the problem by re-authoring Web pages or making adaptations and recommendations according to user preference. Their basis is a good understanding of the relationship between user behaviors and user preference. We propose a practical method to find user’s interest blocks by machine learning using the combination of significant implicit evidences, which is extracted from four aspects of user behaviors: display time, viewing information items, scrolling and link selection. We also develop a customized Web browser for small screen devices to collect user behaviors accurately. For evaluation, we conduct an on-line user study and make statistical analysis based on the dataset, which shows that most types of the suggested implicit evidences are significant, and viewing information items is the least indicative aspect of user behaviors. The dataset is then processed off-line to find user’s interest blocks using the proposed method. Experimental results demonstrate the effectiveness of finding user’s interest blocks by machine learning using the combination of significant implicit evidences. Further analysis reveals the great effect of users and moderate effect of Websites on the usefulness of significant implicit evidences.

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  1. Department of Computer Science and Technology, Tsinghua University, Beijing, People’s Republic of China

    Xin Yang, Peifeng Xiang & Yuanchun Shi

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  1. Xin Yang
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  2. Peifeng Xiang
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  3. Yuanchun Shi
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Correspondence to Xin Yang.

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Yang, X., Xiang, P. & Shi, Y. Finding User’s Interest Blocks using Significant Implicit Evidence for Web Browsing on Small Screen Devices. World Wide Web 12, 213–234 (2009). https://doi.org/10.1007/s11280-009-0061-9

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  • DOI: https://doi.org/10.1007/s11280-009-0061-9

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