Proceedings of the 35th Hawaii International Conference on System Sciences - 2002
Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services Ting-Peng Liang and Hung-Jen Lai Department of Information Management National Sun Yat-sen University Kaohsiung, Taiwan Email:
[email protected] filtering and recommendation will be reviewed briefly. This is followed by the presentation of our approach and its application in Internet news services. Section 4 presents the experimental design. Findings are shown in Section 5. Section 6 concludes the paper.
Abstract Discovering user interests is a very important task for providing personalized services in electronic commerce. A popular approach is to develop customer profiles from their browsing behavior. In this paper, we present an approach that analyzes the browsing content and time to determine user interests. An empirical study using actual news provided by the China Times shows that the proposed system outperforms the traditional headline news compiled by the news editor in both objective performance indices and customer satisfaction.
2. Literature Review The wide spread of the Internet has created an efficient channel for information dissemination. The information overload, however, becomes a problem. How to reduce unnecessary information and provide customized services becomes an important issue. A few filtering mechanisms have been proposed in the past. A typical one is to ask the reader to report his interest after reading. The system can then build a profile of the reader and make recommendation accordingly. For example, Mock and Vemuri [10] presented the Intelligent News Filtering Organization System (INFOS) that asked each reader to indicate whether he liked the report. The system reorganizes the order of news based on the revealed preference. Results from a pilot test show that INFOS can effectively reduce the reader’s search load. Another approach is behavior-based. For instance, Sakagami and Kamba [17] developed the ANATAGONOMY that learns reading preference from the browsing behavior (e.g., scroll, enlarge windows, etc.) of a user. The system has a learning engine and a scoring engine to produce personalized web news. Information filtering and recommendation can also be performed based on feedback from others. For instance, Konstan, et al. [5] proposed a system, called GroupLens, which summarized the feedback from previous readers to allow the next reader to determine whether to read it. This is called collaborative filtering. A system proposed by Balabanovic and Shoham [1] combines content analysis and collaborative filtering. It takes into account the association between a reader and the theme of a report to identify the discrepancy between different individuals.
1. Introduction The rapid propagation of the Internet, along with the evolution of information technologies, has changed the nature of many businesses. The large amount of transactional data collected from the use of information systems allows a company to better understand customer needs and to integrate the knowledge into their product design and marketing plans. For physical products (e.g., computers and televisions), mass customization and fast response to market needs become critical to remaining competitive. For digital products and services (e.g., news services and other Internet content providers, ICP), personalized services that offer the tailored content to different clients based on their interests become feasible and necessary. In this paper, we propose an approach that builds customer profiles from their browsing behavior recorded by the computer and recommends personal services delivered on the web based on the profiles. The approach is then applied to the Internet news services to evaluate its applicability. The news recommendation system includes components for news structure analysis, customer profile analysis, and personal recommendation mechanism. An empirical study was performed to evaluate the proposed approach. The remainder of the paper is organized as follows. First, literature related to information 1
0-7695-1435-9/02 $17.00 (c) 2002 IEEE
Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS-35’02) 0-7695-1435-9/02 $17.00 © 2002 IEEE
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Proceedings of the 35th Hawaii International Conference on System Sciences - 2002
Aij(pij) shows that the likelihood of object Rj having the attribute Ai is pij. Definition 3: Recency adjustment The recency adjustment is to give higher weights to objects accessed recently than those accessed earlier. The recency weight of an object can be calculated by the following equation: γj(Rj) = g(Dj), Where: γi(Rj) is the recency weight of the object, Rj; Dj is the elapse day of reading Rj; D0
