Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Orienteering in an information landscape: how information seekers get from here to there
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
Information seeking in electronic environments
Information seeking in electronic environments
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
PHOAKS: a system for sharing recommendations
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Information storage and retrieval
Information storage and retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Communications of the ACM
Personalization on the Net using Web mining: introduction
Communications of the ACM
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Learning and Practicing Econometrics: SAS Handbook
Learning and Practicing Econometrics: SAS Handbook
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Research Commentary: Transformational Issues in Researching IS and Net-Enabled Organizations
Information Systems Research
Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior
Information Systems Research
Assessing a Firm's Web Presence: A Heuristic Evaluation Procedure for the Measurement of Usability
Information Systems Research
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Experiences with GroupLens: marking usenet useful again
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The computational geowiki: what, why, and how
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Unified collaborative filtering model based on combination of latent features
Expert Systems with Applications: An International Journal
The role of user mood in movie recommendations
Expert Systems with Applications: An International Journal
Privacy-preserving collaborative recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Personalizing the settings for Cf-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the 1st ACM international workshop on Connected multimedia
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
International Journal of Business Information Systems
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Collaborative filtering (CF) is a personalization technology that generates recommendations for users based on others' evaluations. CF is used by numerous e-commerce Web sites for providing personalized recommendations. Although much research has focused on refining collaborative filtering algorithms, little is known about the effects of user and domain characteristics on the accuracy of collaborative filtering systems. In this study, the effects of two factors—product domain and users' search mode—on the accuracy of CF are investigated. The effects of those factors are tested using data collected from two experiments in two different product domains, and from two large CF datasets, EachMovie and Book-Crossing. The study shows that the search mode of the users strongly influences the accuracy of the recommendations. CF works better when users look for specific information than when they search for general information. The accuracy drops significantly when data from different modes are mixed. The study also shows that CF is more accurate for knowledge domains than for consumer product domains. The results of this study imply that for more accurate recommendations, collaborative filtering systems should be able to identify and handle users' mode of search, even within the same domain and user group.