Implicit user profiling for on demand relevance feedback
- 13 January 2004
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 198-205
- https://doi.org/10.1145/964442.964480
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.Keywords
This publication has 11 references indexed in Scilit:
- Evolving agents for personalized information filteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A personalized television listings serviceCommunications of the ACM, 2000
- Personalization on the Net using Web mining: introductionCommunications of the ACM, 2000
- Capturing human intelligence in the netCommunications of the ACM, 2000
- Identifying and tracking changing interestsInternational Journal on Digital Libraries, 1998
- A multilevel approach to intelligent information filteringACM Transactions on Information Systems, 1997
- Recommender systemsCommunications of the ACM, 1997
- Social information filteringPublished by Association for Computing Machinery (ACM) ,1995
- GroupLensPublished by Association for Computing Machinery (ACM) ,1994
- Information filtering and information retrievalCommunications of the ACM, 1992