Implicit user profiling for on demand relevance feedback

@inproceedings{citeulike:1190741, abstract = {In the area of information retrieval and information filtering, relevance feedback is a popular technique which searches similar documents based on the documents browsed by the user. If the user wants to conduct relevance feedback on demand, which means the user wants to see similar documents while reading a document, the existing user profiling techniques cannot acquire keywords in high precision that the user is interested in at such a short time. This paper proposes a method for extracting text parts which the user might be interested in from the whole text of the Web page based on the user's mouse operation in the Web browser. The objective of this research is to (1) find what kind of mouse operation represent users' interests, (2) see the effectiveness of the found mouse operation in selecting keywords, and (3) compare our method with tf-idf, which is the most fundamental method used in many user profiling systems. From the user experiment, the precision to select keywords of our method is about 1.4 times compared with that of tf-idf.}, address = {New York, NY, USA}, author = {Hijikata, Yoshinori }, booktitle = {IUI '04: Proceedings of the 9th international conference on Intelligent user interfaces}, citeulike-article-id = {1190741}, doi = {10.1145/964442.964480}, isbn = {1581138156}, keywords = {en, implicit-feedback, personalization, user-profile}, pages = {198--205}, posted-at = {2007-10-08 19:31:12}, priority = {2}, publisher = {ACM Press}, title = {Implicit user profiling for on demand relevance feedback}, url = {http://portal.acm.org/citation.cfm?id=964480}, year = {2004} }

TY - CONF ID - citeulike:1190741 L3 - citeulike-article-id:1190741 TI - Implicit user profiling for on demand relevance feedback T2 - IUI '04: Proceedings of the 9th international conference on Intelligent user interfaces SP - 198 EP - 205 AD - New York, NY, USA PB - ACM Press SN - 1581138156 N2 - In the area of information retrieval and information filtering, relevance feedback is a popular technique which searches similar documents based on the documents browsed by the user. If the user wants to conduct relevance feedback on demand, which means the user wants to see similar documents while reading a document, the existing user profiling techniques cannot acquire keywords in high precision that the user is interested in at such a short time. This paper proposes a method for extracting text parts which the user might be interested in from the whole text of the Web page based on the user's mouse operation in the Web browser. The objective of this research is to (1) find what kind of mouse operation represent users' interests, (2) see the effectiveness of the found mouse operation in selecting keywords, and (3) compare our method with tf-idf, which is the most fundamental method used in many user profiling systems. From the user experiment, the precision to select keywords of our method is about 1.4 times compared with that of tf-idf. KW - en KW - implicit-feedback KW - personalization KW - user-profile AU - Hijikata, Yoshinori PY - 2004/// UR - http://portal.acm.org/citation.cfm?id=964480 ER -