Special Topics CS 8803
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This 0 page Class Notes was uploaded by Alayna Veum on Monday November 2, 2015. The Class Notes belongs to CS 8803 at Georgia Institute of Technology - Main Campus taught by Staff in Fall. Since its upload, it has received 14 views. For similar materials see /class/234094/cs-8803-georgia-institute-of-technology-main-campus in ComputerScienence at Georgia Institute of Technology - Main Campus.
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Date Created: 11/02/15
Paul Resnick and Hal R Varian Guest Editors Recommender Sy tems TIS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives In everyday life we rely on recommendations from other people either by word of mouth rec ommendation letters movie and book reviews printed in newspapers or general surveys such as ngdt y restaurant guides Recommender systems assist and augment this natural social process In a typical recommender sys tem people provide recommendations as inputs which the system then aggregates and directs to appropriate recipients In some cases the primary transformation is in the aggregation in others the system s value lies in its ability to make good matches between the recommenders and those seeking recom mendations The developers of the first recommender system Tapestry l1 coined the phrase collaborative filtering and several others have adopted it We prefer the more general term recommender system for two rea sons First recommenders may not explictly collaborate with recipients who may be unknown to each other Second recom mendations may suggest particularly interesting items in addition to indicat ing those that should be filtered out This special section includes descrip tions of five recommender systems A sixth article analyzes incentives for provision of rec ommendations Figure 1 places the systems in a technical design space defined by five dimensions First the contents of an evaluation can be anything from a single bit rec ommended or not to unstructured textual annota tions Second recommendations may be entered explicitly but several systems gather implicit evalua tions GroupLens monitors users reading times PHOAKS mines Usenet articles for mentions of 5 6 March I997Vol 40 No 3 COMMUNICA110NS OF THE ACM URLs and Siteseer mines personal bookmark lists Third recommendations may be anonymous tagged with the source s identity or tagged with a pseudo nym The fourth dimension and one of the richest areas for exploration is how to aggregate evaluations GroupLens PHOAKS and Siteseer employ variants on weighted voting Fab takes that one step further to combine evaluations with content analysis Referral Web combines suggested links between people to form longer referral chains Finally the perhaps aggregated evaluations may be used in several ways negative rec ommendations may be filtered out the items may be sorted according to numeric evaluations or evaluations may accompany items in a dis play Figures 2 and 3 identify dimensions of the domain space The kinds of items being recommended and the people among whom evaluations are shared Consider first the domain of items The sheer volume is an important variable Detailed textual reviews of restau rants or movies may be practical but applying the same approach to thousands of daily Netnews mes sages would not Ephemeral media such as netnews most news servers throw away articles after one or two weeks place a premium on gathering and distributing evaluations quickly while evaluations for 19th century books can be gathered at a more leisurely pace The last dimension describes the cost structure of choices people make about the items Is it very costly to miss a good item or sample a bad one How do those costs compare to the bene ts of hitting a good One This cost Structure is likely to interactWith techniCal design choices For example when the costs of incorrectdeci sions are high as they would be say with evaluations of medical treatments evaluations that convey more nuances are likely to be more useful Next Consider the set of reco mrnendations and the people providing aan them W 4 recommendations Do they tend to evaluate many items in common leading to a dense set of recommen dations How man consumers are there and do their tastes vary These factors also will interact with techni cal choices For e39Xample matching people by tastesl automatically is far more valuable in a largerrset of peo ple who may not know 39each other Personalized aggre gation of recommendations will be more valuable when people39s tastes differ than when there are a few experts Social Implications Recomndender systems introduce two interesting incentive problems First once one has established a profile ofinterests it is easy to free ride by consuming evaluations provided by others Moreover as Avery and Zeckhauser argue this problem is not entirely solved even if evaluations are gathered implicitly from exist ing resources or from monitoring user behavior Future systems will likely need to offer some incentive for the provision of recommendations by making it a prereq uisite for receiving recommendations or by offering monetaryquotcompensation Second if anyone can provide recommendations content owners may generate mountains of positive recommendations for their own materials and negative recommendations for their competitors Future systems are likely to introduce precautions that discourage the vote early and oftenquot phenOmenon D 39 1 systems also raise concerns about personal privacy 39In general the more information individuals have about the recommendations the bet ter they will be able to evaluate those recommenda tions However people may not want their habits or views widely knoWn Some recommender systems per mit anonymous participation or participation under a pseudonym but this is not a complete solution since some people may desire an intermediate blend of pri vacyv and attributed credit for their efforts BOth incentive and privacy problems arise in an evaantion sharing system familiar to our readers the peer review system used in academia With respect to incentives every editor knows the best source for a prompt and careful review is an author who currently has an article under consideration With respect to pri vacy blind and doub e blind refereeing are common practices These practices evolved to solve problems inherent to the refereeing process and it may be worth while to consider ways to incorporate such practices into automated systems Figure I c I E I ontents o x icit The recommendation enFtry39 Anonymous Aggregat39on recommendations technical deSign GroupLens a numeric 5 a explicit pseudonymous personalized display alongside space weighting based articles in b seconds b monitor on past existing summary reading agreement views time among recommenders Fab numeric 7 explicit pseudonymous personalized selection 39 ing filtering combined with content analysis ReferralWeb mention of a mined attributed assemble referral display person or a from chain to desired d public data person sources PHOAKS mention of a mined attributed one person one sorted display URL vote per URL usenet postings Siteseer mention of a mined anonymous frequency of display URL from mention in existing overlapping bookmark fold rs folders COMMUNICATIONS oF THE AcM March 997Vol 40 No 3 5 7 FUTURE SYSTEMS WILL LIKELY NEED TO OFFER SOME INCENTIVE FOR PROVIDING RECOMMENDATIONS
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