{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:52:41Z","timestamp":1750308761642,"version":"3.41.0"},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2007,8,1]],"date-time":"2007-08-01T00:00:00Z","timestamp":1185926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2007,8]]},"abstract":"<jats:p>\n            Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a characterization of nearest-neighbor collaborative filtering that allows us to disaggregate global recommender performance measures into contributions made by each individual rating. In particular, we formulate three roles---\n            <jats:italic>scouts<\/jats:italic>\n            ,\n            <jats:italic>promoters<\/jats:italic>\n            , and\n            <jats:italic>connectors<\/jats:italic>\n            ---that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected, respectively. These roles find direct uses in improving recommendations for users, in better targeting of items and, most importantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute (or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the system to attacks such as shilling. We argue that the three rating roles presented here provide broad primitives to manage a recommender system and its community.\n          <\/jats:p>","DOI":"10.1145\/1255438.1255440","type":"journal-article","created":{"date-parts":[[2007,9,14]],"date-time":"2007-09-14T13:44:55Z","timestamp":1189777495000},"page":"8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Scouts, promoters, and connectors"],"prefix":"10.1145","volume":"1","author":[{"given":"Bharath Kumar","family":"Mohan","sequence":"first","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}]},{"given":"Benjamin J.","family":"Keller","sequence":"additional","affiliation":[{"name":"Eastern Michigan University, Ypsilanti, MI"}]},{"given":"Naren","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA"}]}],"member":"320","published-online":{"date-parts":[[2007,8]]},"reference":[{"volume-title":"Proceedings of CHI. 585--592","author":"Cosley D.","key":"e_1_2_1_1_1","unstructured":"Cosley , D. , Lam , S. , Albert , I. , Konstan , J. , and Riedl , J . 2001. Is seeing believing?: How recommender system interfaces affect user's opinions . In Proceedings of CHI. 585--592 . 10.1145\/642611.642713 Cosley, D., Lam, S., Albert, I., Konstan, J., and Riedl, J. 2001. Is seeing believing?: How recommender system interfaces affect user's opinions. In Proceedings of CHI. 585--592. 10.1145\/642611.642713"},{"volume-title":"Proceedings of SIGIR. 230--237","author":"Herlocker J. L.","key":"e_1_2_1_2_1","unstructured":"Herlocker , J. L. , Konstan , J. A. , Borchers , A. , and Riedl , J . 1999. An algorithmic framework for performing collaborative filtering . In Proceedings of SIGIR. 230--237 . 10.1145\/312624.312682 Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of SIGIR. 230--237. 10.1145\/312624.312682"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Konstan J. A. 2003. Personal communication.  Konstan J. A. 2003. Personal communication.","DOI":"10.1145\/3263515"},{"volume-title":"Proceedings of the 13th International World Wide Web Conference. ACM Press","author":"Lam S. K.","key":"e_1_2_1_5_1","unstructured":"Lam , S. K. and Riedl , J . 2004. Shilling recommender systems for fun and profit . In Proceedings of the 13th International World Wide Web Conference. ACM Press , New York, NY, 393--402. 10.1145\/988672.988726 Lam, S. K. and Riedl, J. 2004. Shilling recommender systems for fun and profit. In Proceedings of the 13th International World Wide Web Conference. ACM Press, New York, NY, 393--402. 10.1145\/988672.988726"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.181"},{"volume-title":"Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 329--336","author":"McLaughlin M. R.","key":"e_1_2_1_7_1","unstructured":"McLaughlin , M. R. and Herlocker , J. L . 2004. A collaborative filtering algorithm and evaluation metric that accurately model the user experience . In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 329--336 . 10.1145\/1008992.1009050 McLaughlin, M. R. and Herlocker, J. L. 2004. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 329--336. 10.1145\/1008992.1009050"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1031114.1031116"},{"volume-title":"Proceedings of the Seventh ACM Conference on Electronic Commerce (EC'06)","author":"Mohan B. K.","key":"e_1_2_1_9_1","unstructured":"Mohan , B. K. , Keller , B. K. , and Ramakrishnan , N . 2006. Scouts, promoters, and connectors: The roles of ratings in nearest-neighbor collaborative filtering , In Proceedings of the Seventh ACM Conference on Electronic Commerce (EC'06) , (June). 250--259. 10.1145\/1134707.1134735 Mohan, B. K., Keller, B. K., and Ramakrishnan, N. 2006. Scouts, promoters, and connectors: The roles of ratings in nearest-neighbor collaborative filtering, In Proceedings of the Seventh ACM Conference on Electronic Commerce (EC'06), (June). 250--259. 10.1145\/1134707.1134735"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 2002 Conference on Intelligent User Interfaces (IUI","author":"Rashid A. M.","year":"2002","unstructured":"Rashid , A. M. , Albert , I. , Cosley , D. , Lam , S. , McNee , S. , Konstan , J. A. , and Riedl , J . 2002. Getting to know you: Learning new user preferences in recommender systems . In Proceedings of the 2002 Conference on Intelligent User Interfaces (IUI 2002 ). 127--134. 10.1145\/502716.502737 Rashid, A. M., Albert, I., Cosley, D., Lam, S., McNee, S., Konstan, J. A., and Riedl, J. 2002. Getting to know you: Learning new user preferences in recommender systems. 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