Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Studying the effect of similarity in online task-focused interactions
GROUP '03 Proceedings of the 2003 international ACM SIGGROUP conference on Supporting group work
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Recommender Systems Research: A Connection-Centric Survey
Journal of Intelligent Information Systems
Proceedings of the 10th international conference on Intelligent user interfaces
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Impact of social influence in e-commerce decision making
Proceedings of the ninth international conference on Electronic commerce
Recommending topics for self-descriptions in online user profiles
Proceedings of the 2008 ACM conference on Recommender systems
Bridging gaps by cooperation engineering
Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
Discovery-oriented collaborative filtering for improving user satisfaction
Proceedings of the 14th international conference on Intelligent user interfaces
Contextualized Recommendation Based on Reality Mining From Mobile Subscribers
Cybernetics and Systems
"The devil you know knows best": how online recommendations can benefit from social networking
BCS-HCI '07 Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI...but not as we know it - Volume 1
Expert Systems with Applications: An International Journal
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Same places, same things, same people?: mining user similarity on social media
Proceedings of the 2010 ACM conference on Computer supported cooperative work
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
An intelligent web recommendation system for ubiquitous geolocation awareness
International Journal of Ad Hoc and Ubiquitous Computing
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Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers currently struggle to evaluate the appropriateness of recommendations offered. With collaborative filtering, recommendations are based on people's ratings of items. In this paper, we propose that the usefulness of recommender systems can be improved by including more information about recommenders. We conducted a laboratory online experiment with 100 participants simulating a movie recommender system to determine how familiarity of the recommender, profile similarity between decision-maker and recommender, and rating overlap with a particular recommender influence the choices of decision-makers in such a context. While familiarity in this experiment did not affect the participants' choices, profile similarity and rating overlap had a significant influence. These results help us understand the decision-making processes in an online context and form the basis for user-centered social recommender system design.