Trust in recommender systems Export

@inproceedings{citeulike:83823, abstract = {Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.}, author = {O'Donovan, John and Smyth, Barry}, booktitle = {IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces}, citeulike-article-id = {83823}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1040830.1040870}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1040830.1040870}, doi = {10.1145/1040830.1040870}, isbn = {1581138946}, keywords = {recommender\_systems, trust}, pages = {167--174}, posted-at = {2009-10-27 08:27:48}, priority = {2}, publisher = {ACM Press}, title = {Trust in recommender systems}, url = {http://dx.doi.org/10.1145/1040830.1040870}, year = {2005} }

TY - CONF ID - citeulike:83823 L3 - citeulike-article-id:83823 N2 - Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation. SN - 1581138946 EP - 174 TI - Trust in recommender systems PB - ACM Press T2 - IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces SP - 167 KW - recommender_systems KW - trust AU - O'Donovan, John AU - Smyth, Barry PY - 2005/// UR - http://dx.doi.org/10.1145/1040830.1040870 ER -