Personalized Web Search For Improving Retrieval Effectiveness

@article{citeulike:1217951, abstract = {Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.}, address = {Piscataway, NJ, USA}, author = {Liu, Fang and Yu, Clement and Meng, Weiyi }, citeulike-article-id = {1217951}, doi = {10.1109/TKDE.2004.1264820}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {adaptive-search, en, user-profile}, month = {January}, number = {1}, pages = {28--40}, posted-at = {2007-05-01 03:10:54}, priority = {2}, publisher = {IEEE Educational Activities Department}, title = {Personalized Web Search For Improving Retrieval Effectiveness}, url = {http://portal.acm.org/citation.cfm?id=956410}, volume = {16}, year = {2004} }

TY - JOUR ID - citeulike:1217951 L3 - citeulike-article-id:1217951 TI - Personalized Web Search For Improving Retrieval Effectiveness JF - IEEE Transactions on Knowledge and Data Engineering VL - 16 IS - 1 SP - 28 EP - 40 AD - Piscataway, NJ, USA PB - IEEE Educational Activities Department SN - 1041-4347 N2 - Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient. KW - adaptive-search KW - en KW - user-profile AU - Liu, Fang AU - Yu, Clement AU - Meng, Weiyi PY - 2004/01// UR - http://portal.acm.org/citation.cfm?id=956410 ER -