Effective personalization based on association rule discovery from web usage data Export

@inproceedings{502935, abstract = {To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Dai, Honghua and Luo, Tao and Nakagawa, Miki}, booktitle = {WIDM '01: Proceedings of the 3rd international workshop on Web information and data management}, citeulike-article-id = {1338869}, citeulike-linkout-0 = {http://maya.cs.depaul.edu/~mobasher/papers/widm01.pdf}, citeulike-linkout-1 = {http://dx.doi.org/http://doi.acm.org/10.1145/502932.502935}, doi = {http://doi.acm.org/10.1145/502932.502935}, keywords = {association-rules, clickstream\_data, web-usage-mining}, pages = {9--15}, posted-at = {2007-05-28 17:52:44}, priority = {0}, publisher = {ACM Press}, title = {Effective personalization based on association rule discovery from web usage data}, url = {http://dx.doi.org/http://doi.acm.org/10.1145/502932.502935}, year = {2001} }

TY - CONF ID - 502935 L3 - citeulike-article-id:1338869 N2 - To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy. AD - New York, NY, USA EP - 15 TI - Effective personalization based on association rule discovery from web usage data PB - ACM Press T2 - WIDM '01: Proceedings of the 3rd international workshop on Web information and data management SP - 9 KW - association-rules KW - clickstream_data KW - web-usage-mining AU - Mobasher, Bamshad AU - Dai, Honghua AU - Luo, Tao AU - Nakagawa, Miki PY - 2001/// UR - http://dx.doi.org/http://doi.acm.org/10.1145/502932.502935 ER -