Paper Info
Title
Search Personalization Via Aggregation Of Multidimensional Evidence About User Interests
Abstract
A core aspect of search personalization is inferring the user's search interests. Different approaches may consider different aspects of user information and may have different interpretations of the notion of interest. This may lead to learning disparate characteristics of a user. Although search engines collect a variety of information about their users, the following question remains unanswered: to what extent can personalized search systems harness these information sources to capture multiple views of the user's interests, and adapt the search accordingly? To answer this question, this paper proposes a hybrid approach for search personalization. The advantage of this approach is that it can flexibly combine multiple sources of user information, and incorporate multiple aspects of user interests. Experimental results demonstrate the effectiveness of the proposed approach for search personalization.
Year
DOI
Venue
2015
10.1007/978-3-319-18117-2_34
COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING (CICLING 2015), PT II
Field
DocType
Volume
World Wide Web,Personalized search,Search engine,Information retrieval,Computer science,User information,Personalization
Conference
9042
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
17
3
Name
Order
Citations
PageRank
Yu Xu172.13
M. Rami Ghorab2698.08
Séamus Lawless311130.18