{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T12:40:33Z","timestamp":1694608833446},"reference-count":38,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2011,10,6]],"date-time":"2011-10-06T00:00:00Z","timestamp":1317859200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Softw Pract Exp"],"published-print":{"date-parts":[[2013,10]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complementary results that are tailored to the user's expectations. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and that considers feedback to build user\u2010centric and document\u2010centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required <jats:italic>information diversity<\/jats:italic> and enhance the user search experience by presenting additional results tailored to the user's interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Moreover, collecting the recommendation data poses the problem of users\u2019 privacy, and the bias one peer can induce to the system by sending fake recommendations. To that end, CoFeed ensures both publisher anonymity and rate limitation. With the former, the origin of the data is never known by the server that processes it, even if several servers collude to spy on some user. The latter, combined with decoupled authentication, allows to minimize the influence of cheating peers sending fake recommendations. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balancing. Copyright \u00a9 2011 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/spe.1127","type":"journal-article","created":{"date-parts":[[2011,10,6]],"date-time":"2011-10-06T07:42:37Z","timestamp":1317886957000},"page":"1165-1184","source":"Crossref","is-referenced-by-count":2,"title":["CoFeed: privacy\u2010preserving Web search recommendation based on collaborative aggregation of interest feedback"],"prefix":"10.1002","volume":"43","author":[{"given":"Pascal","family":"Felber","sequence":"first","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]},{"given":"Peter","family":"Kropf","sequence":"additional","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]},{"given":"Lorenzo","family":"Leonini","sequence":"additional","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]},{"given":"Toan","family":"Luu","sequence":"additional","affiliation":[{"name":"EPFL  Lausanne Switzerland"}]},{"given":"Martin","family":"Rajman","sequence":"additional","affiliation":[{"name":"EPFL  Lausanne Switzerland"}]},{"given":"Etienne","family":"Rivi\u00e8re","sequence":"additional","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]},{"given":"Valerio","family":"Schiavoni","sequence":"additional","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]},{"given":"Jos\u00e9","family":"Valerio","sequence":"additional","affiliation":[{"name":"Institut d'Informatique Universit\u00e9 de Neuch\u00e2tel  Switzerland"}]}],"member":"311","published-online":{"date-parts":[[2011,10,6]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"e_1_2_12_3_1","unstructured":"Available from:http:\/\/googleblog.blogspot.com\/2008\/11\/searchwiki\u2010make\u2010search\u2010your \u2010own.html."},{"key":"e_1_2_12_4_1","unstructured":"GylfasonH KhanO SchoenebeckG.Chora: expert\u2010based P2P Web search.Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201906) Hakodate Japan 2006."},{"key":"e_1_2_12_5_1","unstructured":"MisloveA GummadiKP DruschelP.Exploiting social networks for internet search.Proceedings of the 5th workshop on Hot Topics in Networks (ACM HotNets\u2010V) Irvine California 2006."},{"key":"e_1_2_12_6_1","doi-asserted-by":"crossref","unstructured":"FelberP RajmanM Rivi\u00e8reE SchiavoniV ValerioJ.SPADS: publisher anonymization for DHT storage.>Proceedings of the 10th IEEE International Conference on Peer\u2010to\u2010Peer Computing (IEEE P2P\u201910) Delft The Netherlands 2010.","DOI":"10.1109\/P2P.2010.5569985"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/362686.362692"},{"key":"e_1_2_12_8_1","unstructured":"ChengK XiangL IwaiharaM XuH MohaniaMM.Time\u2010decaying bloom filters for data streams with skewed distributions.Proceedings of the 15th International Workshop on research Issues in Data Engineering (RIDE\u2010SDMA 2005) Stream Data Mining and Applications Tokyo Japan April2005. 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