Proceedings ArticleDOI
Personalized web search by mapping user queries to categories
Fang Liu,Clement Yu,Weiyi Meng +2 more
- pp 558-565
Reads0
Chats0
TLDR
A novel technique to map a user query to a set of categories, which represent the user's search intention, is proposed, which can serve as a context to disambiguate the words in the users' query.Abstract:
Current web search engines are built to serve all users, independent of the 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 map a user query to a set of categories, which represent the user's search intention. This set of categories can serve as a context to disambiguate the words in the user's query. 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. Several learning and combining algorithms are evaluated and found to be effective. Among the algorithms to learn a user profile, we choose the Rocchio-based method for its simplicity, efficiency and its ability to be adaptive. Experimental results indicate that our technique to personalize web search is both effective and efficient.read more
Citations
More filters
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Proceedings ArticleDOI
Personalizing search via automated analysis of interests and activities
TL;DR: This research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search.
Journal ArticleDOI
Implicit feedback for inferring user preference: a bibliography
Diane Kelly,Jaime Teevan +1 more
TL;DR: Traditional relevance feedback methods require that users explicitly give feedback by specifying keywords, selecting and marking documents, or answering questions about their interests, which can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.
Proceedings ArticleDOI
A large-scale evaluation and analysis of personalized search strategies
TL;DR: It is revealed that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries, and even harms search accuracy under some situations.
Book ChapterDOI
User profiles for personalized information access
TL;DR: This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles and discusses in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts.
References
More filters
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.