Is seeing believing?: how recommender system interfaces affect users' opinions Export

@inproceedings{citeulike:344219, abstract = {Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.}, address = {New York, NY, USA}, author = {Cosley, Dan and Lam, Shyong K. and Albert, Istvan and Konstan, Joseph A. and Riedl, John}, booktitle = {CHI '03: Proceedings of the SIGCHI conference on Human factors in computing systems}, citeulike-article-id = {344219}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=642713}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/642611.642713}, comment = {displaying the consensus ranking while a user evaluates a contribution is likely to influence his opinion}, doi = {10.1145/642611.642713}, isbn = {1-58113-630-7}, keywords = {hci, ranking}, location = {Ft. Lauderdale, Florida, USA}, pages = {585--592}, posted-at = {2009-06-16 12:29:11}, priority = {1}, publisher = {ACM}, title = {Is seeing believing?: how recommender system interfaces affect users' opinions}, url = {http://dx.doi.org/10.1145/642611.642713}, year = {2003} }

TY - CONF ID - citeulike:344219 L3 - citeulike-article-id:344219 N2 - Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings. CY - Ft. Lauderdale, Florida, USA SN - 1-58113-630-7 AD - New York, NY, USA EP - 592 TI - Is seeing believing?: how recommender system interfaces affect users' opinions PB - ACM T2 - CHI '03: Proceedings of the SIGCHI conference on Human factors in computing systems SP - 585 KW - hci KW - ranking N1 - displaying the consensus ranking while a user evaluates a contribution is likely to influence his opinion AU - Cosley, Dan AU - Lam, Shyong AU - Albert, Istvan AU - Konstan, Joseph AU - Riedl, John PY - 2003/// UR - http://dx.doi.org/10.1145/642611.642713 ER -