Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
SERF: integrating human recommendations with search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
EGOVIS'11 Proceedings of the Second international conference on Electronic government and the information systems perspective
Peer-based relay scheme of collaborative filtering for research literature
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
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This paper presents the SERF (System for Electronic Recommendation Filtering) which is a collaborative filtering system that recommends context-sensitive, high-quality information sources for document search. Collaborative filtering systems remove the limitation of traditional content-based search by using individual's ratings to evaluate and recommend information sources. SERF uses collaborative filtering algorithms to predict the relevance and quality of each document with respect to each particular user and their specific information need. In our system, users specify their need in the form of a natural language query, and are provided with recommended documents based on ratings by other users with similar questions. Preliminary experiments show that the collaborative filtering recommendations increase the efficiency of the document search process. We also discuss some key challenges of designing a collaborative filtering system for document search.