Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
International Journal of Human-Computer Studies
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Improving collaborative recommender systems by means of user profiles
Designing personalized user experiences in eCommerce
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Editorial: Measuring the impact of personalization and recommendation on user behaviour
International Journal of Human-Computer Studies
Adaptive algorithm based on clustering techniques for custom reading plans
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
One critical question suggested by Web 2.0 is as follows: When is it better to leverage the knowledge of other users vs. rely on the product characteristic-based metrics for online product recommenders? Three recent and notable changes of recommender systems have been as follows: (1) a shift from characteristic-based recommendation algorithms to social-based recommendation algorithms; (2) an increase in the number of dimensions on which algorithms are based; and (3) availability of products that cannot be examined for quality before purchase. The combination of these elements is affecting users' perceptions and attitudes regarding recommender systems and the products recommended by them, but the psychological effects of these trends remain unexplored. The current study empirically examines the effects of these elements, using a 2 (recommendation approach: content-based vs. collaborative-based, within)x2 (dimensions used to generate recommendations: 6 vs. 30, between)x2 (product type: experience products (fragrances) vs. search products (rugs), between) Web-based study (N=80). Participants were told that they would use two recommender systems distinguished by recommendation approach (in fact, the recommendations were identical). There were no substantive main effects, but all three variables exhibited two-way interactions, indicating that design strategies must be grounded in a multi-dimensional understanding of these variables. The implications of this research for the psychology and design of recommender systems are presented.