Integrating Web Usage and Content Mining for More Effective Personalization

By Bamshad Mobasher, Honghua (Kathy) Dai, Tao Luo, Jiang Zhu. International Conference on Electronic Commerce and Web Technologies, 2000

323 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a199cf5841d2381b2978848497e0e44ba0b15db4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jae Kyeong Kim
2. Soung Hie Kim
3. Yoon Ho Cho
4. Dimitrios Pierrakos
5. Constantine D. Spyropoulos
6. Christos Papatheodorou
7. G. Paliouras
8. Wai Lam

In Information Systems

1. Shyam Sundar
2. S. Kalyanaraman
3. Jae Kyeong Kim
4. Soung Hie Kim
5. Yoon Ho Cho
6. G. Paliouras
7. Constantine D. Spyropoulos
8. Dimitrios Pierrakos

In Sociology and Political Science

1. S. Kalyanaraman
2. Shyam Sundar
3. Bamshad Mobasher
4. Kristopher J Preacher
5. Andrew F. Hayes
6. Riva H. Tukachinsky

Citations in the corpus

• Web Usage Mining as a Tool for Personalization: A Survey (2003, methodology): “This approach to Web personalization follows WEB USAGE MINING 347 the same basic principle that was proposed by Mobasher et al. (2000b), i.e., the separation of the system into an online and an o¥ine part.”
• A personalized recommender system based on web usage mining and decision tree induction (2002, methodology): “The pattern discovery tasks involve the discovery of association rules, sequential patterns, usage clusters, page clusters, user classifications or any other pattern discovery method (Mobasher et al., 2000a,b).”
• The Psychological Appeal of Personalized Content in Web Portals: Does Customization Affect Attitudes and Behavior? (2006, background): “Web interfaces employ to provide customized information (Pierrakos, Paliouras, Papatheodorou, & Spyropoulos, 2003; see also Mobasher, Dai, Luo, Sung, & Zhu, 2000 ).”
• Web mining for web personalization (2003, unknown): “The most advanced system is the WebPersonalizer, proposed by Mobasher et al.”
• Data Mining for Web Personalization (2007, background): “For example, the results of user-based clustering can be combined with “content profiles” derived from the clustering of content features in pages [87].”
• Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007, background): “As shown in Mobasher, Dai, Luo, and Nakagawa (2000) , traditional Web personalization strategies could be problematic when a big site evolves or surfers’ browsing behavior changes over time.”