{"id":317303,"date":"2016-11-07T12:09:54","date_gmt":"2016-11-07T20:09:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=317303"},"modified":"2018-10-16T20:07:52","modified_gmt":"2018-10-17T03:07:52","slug":"trust-based-recommendation-systems-axiomatic-approach","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/trust-based-recommendation-systems-axiomatic-approach\/","title":{"rendered":"Trust-based Recommendation Systems: An Axiomatic Approach"},"content":{"rendered":"<p>High-quality, personalized recommendations are a key fea-<br \/>\nture in many online systems. Since these systems often have<br \/>\nexplicit knowledge of social network structures, the recom-<br \/>\nmendations may incorporate this information. This paper<br \/>\nfocuses on networks which represent trust and recommen-<br \/>\ndations which incorporate trust relationships. The goal of<br \/>\na trust-based recommendation system is to generate per-<br \/>\nsonalized recommendations from known opinions and trust<br \/>\nrelationships.<br \/>\nIn analogy to prior work on voting and ranking systems,<br \/>\nwe use the axiomatic approach from the theory of social<br \/>\nchoice. We develop an natural set of five axioms which we<br \/>\ndesire any recommendation system exhibit. Then we show<br \/>\nthat no system can simultaneously satisfy all these axioms.<br \/>\nWe also exhibit systems which satisfy any four of the five<br \/>\naxioms. Next we consider ways of weakening the axioms,<br \/>\nwhich can lead to a unique recommendation system based on<br \/>\nrandom walks. We consider other recommendation systems<br \/>\n(personal page rank, majority of majorities, and min cut)<br \/>\nand search for alternative axiomatizations which uniquely<br \/>\ncharacterize these systems.<br \/>\nFinally, we determine which of these systems are incen-<br \/>\ntive compatible. This is an important property for systems<br \/>\ndeployed in a monetized environment: groups of agents in-<br \/>\nterested in manipulating recommendations to make others<br \/>\nshare their opinion have nothing to gain from lying about<br \/>\ntheir votes or their trust links.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>High-quality, personalized recommendations are a key fea- ture in many online systems. Since these systems often have explicit knowledge of social network structures, the recom- mendations may incorporate this information. This paper focuses on networks which represent trust and recommen- dations which incorporate trust relationships. The goal of a trust-based recommendation system is to generate [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"WWW 2008","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 17th international conference on World Wide Web (WWW)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"199-208","msr_page_range_start":"199","msr_page_range_end":"208","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 17th international conference on World Wide Web 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