Arecommender system is a software application capable ofsuggesting interesting things to its users after learningtheir preferences over time (Jannach et al. 2010, Ricci et

al. 2011). Recommender systems were envisioned in the 1970s(Negroponte 1970), conceptualized and prototyped in the early1990s (Goldberg et al. 1992), and implemented and first com-mercialized in the mid-1990s (Resnick and Varian 1997). Theyhave two (sometimes diametrically opposed) value propositionsthat contribute to their popularity. On one hand, they helptheir users cope with the problem of information overload (thatis, they take the users’ side). On the other hand, they providecompanies that operate them with an effective way to drivemore sales or increase the level of engagement of the servicesthat they offer (that is, they take the business’s side).

Multiple vendors have taken recommender systems to marketin a series of waves over the last two decades. The first two wavescan respectively be matched with the web 1.0 and 2.0 revolu-tions. Both waves introduced dozens of companies selling rec-ommendation technologies (see table 1) and thousands of com-panies using recommender systems with the final goal ofincreasing their revenues. Not all of the recommender vendorsthat started during these waves are still active. Nevertheless, rec-

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FALL 2011 19Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602

The Big Promise of Recommender Systems

Francisco J. Martin, Justin Donaldson, Adam Ashenfelter, Marc Torrens, and Rick Hangartner

n Recommender systems have been part of theInternet for almost two decades. Dozens of ven-dors have built recommendation technologiesand taken them to market in two waves, rough-ly aligning with the web 1.0 and 2.0 revolu-tions. Today recommender systems are found ina multitude of online services. They have beendeveloped using a variety of techniques and userinterfaces. They have been nurtured with mil-lions of users’ explicit and implicit preferences(most often with their permission). Frequentlythey provide relevant recommendations thatincrease the revenue or user engagement of theonline services that operate them. However,when we evaluate the current generation of rec-ommender systems from the point of view of the“recommendee,” we find that most recom-mender systems serve the goals of the businessinstead of their users’ interests. Thus we believethat the big promise of recommender systemshas yet to be fulfilled. We foresee a third waveof recommender systems that act directly onbehalf of their users across a range of domainsinstead of acting as a sales assistant. We alsopredict that such new recommender systemswill better deal with information overload, takeadvantage of contextual clues from mobiledevices, and utilize the vast information andcomputation stores available through cloud-computing services to maximize users’ long-term goals.

ommender services are now a ubiquitous part ofmany online services. Dozens of applications cap-ture our explicit and implicit preferences on a dai-ly basis (normally with our permission) with thegoal of recommending something interesting inthe near future (Kirn 2010). Hundreds ofresearchers around the world have contributed toimprove the accuracy, scalability, security, andmany other aspects of recommendation systems(Konstan, Riedl, and Smyth 2007; Pu et al. 2008;Bergman et al. 2009; Amatriain et al. 2010).

However, when we look at the current recom-mender systems generation from the point of viewof the “recommendee” (users’ side) we can see thatrecommender systems are more inclined towardachieving short-term sales and business goals.Instead of helping their users to cope with theproblem of information overload they can actual-ly contribute to information overload by propos-ing recommendations that do not meet the users’current needs or interests. Consider the followingquestions: Could you imagine the Netflix recom-mender suggesting that you watch a TV show thatis broadcasted tonight instead of prompting you tostream another movie from the Netflix repository?Could you imagine the Amazon recommender sug-gesting that you borrow a novel from your friend,who already bought it a few months ago, instead ofrecommending to buy it now? Could you imaginea bank recommender suggesting that you transferyour savings to a certified deposit with higherinterest in another bank? Could you imagine theiTunes recommender suggesting that you stop buy-ing because you are exceeding your monthly budg-et for songs?

The recommendations we just mentioned arehard to imagine because recommender systemshave mostly proliferated on the business’s side.There is no technical reason why a recommendersystem could not make such suggestions by con-sidering a broader view of users’ goals and sourcesof information. In fact recommender systems wereoriginally envisioned to operate this way (Gold-berg et al. 1992; Maes 1994; Resnick et al. 1994;Negroponte 1995) but most vendors favoredlicensing their recommendation technology toretailers over direct-to-consumer initiatives. How-ever, the current confluence of easily programma-ble mobile devices, open APIs, distribution chan-nels (such as the Apple App store), and the low costof cloud-based computing and storage creates anexcellent breeding ground for a new wave ofdirect-to-consumer recommender systems.

One aim of this article is to encourage bothresearchers and practitioners to embark in newcommercial adventures. The window of opportu-nity is now open to innovate in a third generationof recommender systems that act directly onbehalf of their users and help them cope with

information overload. Next we review the first twowaves of recommender systems, share lessonslearned, and describe what we can expect from thenext wave.

The First Wave of Recommender Systems

In the 1980s the increasing use of electronic mailand newsgroups caused the first common infor-mation overload problems (Palme 1984).Researchers started to investigate new methods ofhandling these issues, drawing on theories andtechniques from general information filtering sys-tems (Goldberg et al. 1992; Resnick et al. 1994) andword-of-mouth automation (Shardanand andMaes 1995). At this early stage, researchers alsoinvestigated more sophisticated systems thatworked proactively on behalf of the user in per-sonal computing contexts (Kozierok and Maes1993). Pattie Maes used the metaphor of a personalassistant to exemplify the collaboration between auser and an interface agent capable of learning theuser’s interests, habits, and preferences to copewith the problem of information and work over-load (Maes 1994). As the agent gradually learnshow better to assist its user, the range of tasks thatcan be delegated increases. Common early tasksincluded scheduling meetings and filtering email.

Researchers reasoned that harnessing the collec-tive assessment of many individuals could quicklyestablish abstract notions of priority and classifica-tion. This basic idea was the basis for Tapestry, thefirst experimental system supporting collaborativefiltering. Tapestry was built at the Xerox Palo AltoResearch Center to handle the (relatively) large vol-ume of email received by their researchers at thetime (Goldberg et al. 1992).

In addition to email, collaborative filtering quick-ly found success in a number of domains. The Grou-pLens project used collaborative filtering in order tohelp people find interesting articles in online news-groups (Resnick et al. 1994). In 1995, the GroupLensteam proposed movie recommendations throughthe Movielens project (see figure 1). The Ringo sys-tem at MIT used social information filtering in orderto generate personalized music recommendations(Shardanand and Maes 1995).

It was not long before collaborative filteringtechniques found their way into commercial prod-ucts. In 1995 Firefly Networks (originally Agents,Inc.) became the first company to focus on offeringa recommendation service. Firefly Networks used asuccessor of Ringo that was capable of generatinguser profiles to recommend music. It expanded itsservice to recommend movies and websites as well.

As the 1990s ended, more and more commercialapplications included collaborative filtering, andefforts were made to better monetize the new tech-

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nology. Resnick and Varian (1997) proposed threebusiness models for independent, direct-to-con-sumer recommender systems. These included sub-scriptions or pay-per-use, advertiser support, andcontent-owner support. However, recommendersystems were costly to run and maintain. There-fore, many recommender system startups focusedprimarily on licensing the technology to the bur-geoning e-commerce domain. Net Perceptions,founded by a group of researchers at University ofMinnesota in 1996, quickly became the leadingvendor of business-to-business recommender sys-tems, powering the recommendations of many ofthe key web companies. Likeminds, created in1997 by O’Reilly and AOL, also specialized in com-mercializing collaborative filtering tools to helpother websites offer personalized recommenda-tions. In 1998, Firefly Network was acquired byMicrosoft and its technology seeded Microsoft’sPassport (now known as LiveID).

As the millennium ended, new web portals ande-commerce sites were in fierce competition toattract users, differentiating their services byactively developing their recommendations. Verysoon a number of software manufacturers of appli-cation and content management servers (such asE.piphany, Blue Martini, Vignette, and others)complemented their product suites with recom-mendation or personalization modules. Table 1gives a simple overview of the relevant businesses.By the end of 1999 many popular websites werealready buying recommender system services orwere planning to roll out their own service. How-ever, in March 2000 the dot-com bubble burst andthe e-commerce domain collapsed, with compa-nies losing more than 5 trillion dollars of marketvalue from March 2000 to October 2002.

Although the views and experiences of the rele-

vant vendors may vary to some extent, there are anumber of prominent lessons learned during thefirst wave that shaped the commercial ventures ofthe second wave of recommender systems (seetable 2).

The Second Wave of Recommender Systems

The number of Internet users and the number ofwebsites never stopped growing despite the col-lapse of the Internet sector. At the end of 2000there were more than 250 million Internet sub-

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First Wave of Recommender Systems Vendors

Companies founded between 1991 and 2000

ATG Dynamo (acquired by Oracle), Blue Martini (acquired by Escalate Retail), Braisins, Broadvision, E.piphany (acquired by SSA Global Technologies acquired by Infor), Firefly Network (acquired by Microsoft), IBM WebSphere Personalization, Like Minds (acquired by Andromedia), Manna, Open Sesame (acquired by Bowne Inc.), Net Perceptions, Vignette (acquired by Open Text).

Second Wave of Recommender Systems Vendors

Companies founded between 2001 and 2010

Adobe Recommendations (before Omniture recommendations), Aggregate Knowledge, Amadesa, Avail Intelligence, Barilliance, Bay Note, Blue Know, Certona, Changing Worlds (acquired by Amdocs), Choice Stream, Clever Set (acquired by ATG Dynamo), Commendo, Criteo, Directed Edge, Easyrec, 4 Tell, Gravity, Ericsson SDP, I Go Digital, Iletken, Istobe, July Systems, Loomia, Match Mine, Media Unbound (acquired by Rovi), My Buys, Olista (acquired by Connectiva), Pontis, Predictive Intent, Prediggo, Rich Relevance, 7 Billion People, Strands, We Like, Xiam (acquired by Qualcomm), Xtract.

Table 1. Most of the Key Vendors of Recommender Systems Founded in the Last Two Decades.

Figure 1. A Screenshot from Movielens.

This screenshot is an example of a first-wave recommender system for moviesand the first high quality source of data for recommender system research.(Used with permission.)

scribers (see figure 2). These users became moreeager to share their own content through personalweb pages, blogs, and online communities. Theamount of user-generated content soon surpassedprofessional media content, and the social or web2.0 revolution started. While the market for vari-ous digital media content exploded, other prob-lems soon surfaced as user-generated contentexhibited some of the same issues encountered incollaborative-filtering-based recommender systems(Su and Khoshgoftaa 2009). In particular, problemsrelated to scalability, sparsity, and shilling becamemuch more challenging to solve.

In 2002, 2003, and 2004, Friendster, MySpace,and Facebook were launched (respectively). Manypeople started to amass online friends and broad-cast each detail of their personal lives. Social net-working quickly became a vital personal commu-nication channel as it substituted for email, ShortMessage Service (SMS), or even face-to-face com-munication for many people.

As the usage of their systems grew, web compa-nies started to analyze recommendation algorithmsin order to make them more scalable, robust, andless prone to bias or sabotage. Amazon.com,Netflix, and Yahoo! Music popularized their recom-mender systems and solved many of the scalabilityissues of the first recommender systems (Linden,Smith, and York 2003). Five-star rating schemescombined with purchase and rental behavior pat-terns became a common source of data for most bige-commerce websites. However, despite the growthand improvements of the underlying algorithmsand architecture, recommendation technologieswere still too complex or expensive to be affordableby smaller online retailers.

Rating-based collaborative filtering systems

relied on their users to rate dozens of items beforethe service could provide appropriate suggestions.The systems also tended to be static, with no abil-ity to adapt to a given user’s changing tastes. Thiscaused problems for domains where many itemswere unrated, and a user’s taste could be expectedto change quickly. Music was one such domain,and an important component of the burgeoningdigital media market. This opportunity led to thedevelopment and adoption of techniques thatminimized the number of explicit interactions totrain a recommender, similar to the approachintroduced by the experimental Ringo system sev-eral years prior. Sites like Audio Scrobbler (latermerged with Last.fm), Music Mobs, MOG, andMyStrands started tracking what people were lis-tening to on their computers to tacitly indicateinterest, and to automatically generate social-basedrecommendations with little to no prior informa-tion required from the user.

Pandora (formerly known as the Music GenomeProject) is noteworthy in the way that it integrat-ed a recommender system into its music-streamingservice. Instead of using listening behavior or user-generated playlists to relate music, Pandora useddozens of music experts that rated songs followinghundreds of preestablished attributes. Then it useda basic rating system (thumbs up, thumbs down)to tailor future recommendations for each user.However, despite operating for nearly 10 years,Pandora still can only offer recommendations foraround one million songs. It is currently an impor-tant example in the long debate on the trade-offbetween scalability and the quality of expert-basedrecommendations, which goes beyond the scopeof this article.

In addition to the differences in music recom-

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Integration Is Much Harder Than It Looks Schedule enough time upfront for all the last-mile problems you will !nd. Much more if you try to integrate in a constantly changing environment such as the one many web companies had when initial web technologies were still under development.

Customer’s In-House Competition Is Fiercer Than Market Competition Integrating a recommender system implies parallel battles with the IT and marketing departments in midsize companies. Don’t waste your time with big web companies as they see recommendations as too strategic to be delegated.

Cold Start Is An Issue If Your Algorithm Is Exclusively Fed by the System’s Own Data If the recommender system is purely based on your own data, make sure it is started with a substantial initial amount, or wait to unveil the service until enough data is collected. Otherwise the initial experience for the users will be very poor.

Don’t Forget About Scalability Deciding what processes a recommender system must run online and which of"ine is crucial to be able to scale. The !rst generation of recommender systems tried to solve everything online or with memory-based approaches that did not scale when the number of users or number of items to recommend increased.

Table 2. Some Lessons Learned During the First Wave of Recommender Systems.

mendation approaches, online news also popular-ized different forms of recommender systems. Ini-tially, Findory offered a personalized news serviceutilizing content-based collaborative filtering tech-niques for news story recommendation (figure 3).However, online news sites such as Digg, Reddit,and Hacker News soon became the more popularmeans for finding relevant news. Instead of morepersonalized recommender algorithms, storieswere promoted and recommended primarily byglobal popularity trends.

In 2006 Strands organized a summer school onthe “Present and Future of Recommender Systems”that brought together researchers, practitioners,and students. Building on its success, the Associa-tion for Computing Machinery (ACM) recom-mender systems conference “RecSys” was estab-lished, held in Minneapolis (2007), Lausanne(2008), New York (2009), Barcelona (2010), andChicago (2011) underway. Collaborative filteringhas been the method underlying most recom-mender systems studies, as can be seen in theweighted tag cloud visualization in figure 4.

While research on algorithms has captured mostof the attention for recommender system studies,the rise of social networking indicated that simply

keeping tabs on your friends’ listening, watching,and reading habits is a more natural way to keepinformed of the latest trends. Undirected word-of-mouth suggestions were automatically transmittedthrough systems like Facebook’s News Feed with-out algorithmic processing but embedded in a con-venient interface.

At the end of 2006, Netflix challenged the worldto improve the accuracy of its movie recommen-dation system by 10 percent. It created a contestthat brought lots of attention to recommender sys-tems, not just for the $1,000,000 prize, but for thefact that a popular service like Netflix was willingto invest in such an exotic scheme just to improvethe quality of its recommender service. In 2009 theBellKor “Pragmatic Chaos” team won the contest(Töscher, Jahrer, and Bell 2009). Their efforts mayhave set the bar for a best-in-class recommenda-tion algorithm for movies, but practitioners havenoted that algorithmic precision is just one ofmany factors that affect a user’s adoption of a rec-ommendation, and other issues such as interfacedesign, long-term performance evaluation, or con-text-awareness are also prominent parts of a rec-ommender system outcome.

A number of companies saw the opportunity

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Dozens of vendors

~ 100 vendors

~ 1000 vendors

Recommendation Technology Cost

Internet Users

Dat

a

Mob

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ubsc

riber

s

1991 2000 2001 2010 20111st Wave 2nd Wave 3rd Wave

2B

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Most content generated by experts —Managing Gigabytes

Most content generated by users —Managing Terabytes

Most content generated by machines —Managing Exabytes

A few thousand $

250M

Figure 2. Internet Trends 1991–2010.

for offering recommendations as an affordablesoftware service (Clever Set, Loomia, Rich Rele-vance, Strands, and others) to smaller web com-panies. See table 1 for a more complete list. Thecommon approach was for the vendor to offer aremote application programming interface (API)that collected user activity on web pages (clickstream, ratings, purchases, and others), and gen-erated on-the-fly recommendations relevant tothe users’ behavior. Despite strong evidence thatdifferent product and service domains (such asmovies, music, news, and others) have differentneeds for recommender services, many software-as-service recommender system vendors general-ized their solutions with only minor tailoring forspecific domains. An additional problem for these

vendors arose when web usage analytics compa-nies started to incorporate the same recom-mender techniques as part of their service offer-ings.

Recommender systems have now become wellstudied both theoretically and practically. Table 3shows a number of lessons learned deploying rec-ommenders during the second wave. However,despite all the progress made, most commercialrecommender systems have taken the business’sside and their recommendations are firmly linkedto short-term sales of products or services insteadof considering real users’ contexts and needs. Thus,we predict a new wave of recommender systems onthe user’s side that will better deal with informa-tion overload.

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Figure 3. A Screenshot from Findory.

This figure is an example of a second-wave recommender system for news articles — a realization of Negroponte’s (1995) “Daily Me”and a precursor of current personalized newspapers for the iPad. (Used with permission.)

The Third Wave of Recommender Systems

With more than 5 billion mobile subscribers, andmore than 2 billion Internet users (see figure 2),we live in a era where information has gone fromscarce to superabundant (Cukier 2010).

Technically savvy individuals are already utiliz-ing a broad range of services, but must manage andprioritize the flows of information and suggestionson their own. On a typical day a given person mayalready be receiving a multitude of recommenda-tions from mobile and nonmobile computing plat-forms. Personal sleep aid systems monitor sleep

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Figure 4. Tag Cloud Created from the Titles of All the Articles Published at the ACM RecSys Conferences Using IBM Wordle.

Hybridization Solves Many of the Issues with Recommender Systems and Wins Contests Too Using exclusively one recommender systems technique produces less robust solutions. An ensemble of techniques has shown to be successful and easily improve accuracy.

Easy to Integrate Also Means Easy to Remove Offering software-as-service recommender systems reduces the burden of integration and makes it easier to get the customers but also makes it easier for the customers to switch to another vendor.

Integration with Customer Care Services Is a Pending Subject You can buy hundreds of products from Amazon or Apple and get better and better recommendations but unbelievably their customer care services do not know if you are a good or bad customer. Integration of CRM and recommender systems is a challenging issue.

Right Balance of Attention Needs to Be Given to Algorithms and User Experience Although most research focuses on algorithms, user interfaces and user experience have a heavier weight on the !nal performance of recommender systems. Algorithms are mature, but an engineering approach to know how and when to recommend is still immature.

Cold Start Is Not a Problem If You Think Out of the Box With the web 2.0 revolution, user-generated content made it possible to overcome the cold-start problem by just considering relatedness among items and people on the social web.

The Best Nearest Neighbor Algorithm Is the Social Graph

Do not spend time calculating nearest neighbors and just let people tell you! Rating-systems-explicit rating systems are very often not well de!ned semantically and lead to poor recommendations. Implicit feedback on actual preferences have resulted in much better recommendations than explicit ratings.

Recommendation Technologies Have Been Commoditized Technologies that were licensable for hundreds or even millions of dollars only 10 years ago are now freely available (See !gure 2). This, together with the high number of players and the dif!culties to evaluate ordifferentiate their products, has been one of the main causes that pushed many vendors out of business as the price for recommendation technologies dropped to levels that made operating their businesses unsustainable.

Table 3. Some Lessons Learned During the Second Wave of Recommender Systems.

such as map-reduce, provide super-computing power at a fraction of itsprevious cost. At the same time, largevarieties of information are being madeavailable online through public APIs.In addition, new computing libraries,frameworks, and services such as SciPy,Apache Mahout, and Google Predic-tion API are making it easier for devel-opers to utilize sophisticated algo-rithms on massive amounts of data.This will enable the third-wave recom-mender system ventures to focus moreon the user experience and less on thetechnical side of algorithms.

User’s Long-Term Goals Finally, third-generation recommendersystems will have a greater emphasison handling long-term user goals andconstraints, such as monthly budgetlimits, or scheduling constraints on theuser’s personal calendar. Most currentrecommendation systems focus onshort-term interactions: cross-selling,up-selling, or suggesting media forimmediate consumption. Removingthe emphasis on short-term profit willallow recommendation systems toencourage discovery and, we hope,maximize a user’s long-term satisfac-tion.

Conclusion We characterize two basic types of rec-ommender systems that have beendeployed in practice. The first typeincludes recommenders that take theuser’s side (such as Siri) and are operat-ed on the user’s computer (smartphoneor tablet) by a completely independentservice provider. The second type cor-responds to recommenders that takethe business side (for example, Ama-zon’s recommender). These recom-menders are put in place and operatedby the retailer of the product being rec-ommended or operated on behalf ofthe retailer.

As technology advances and recom-mender systems become more com-monplace for the public, we may nowbe on the verge of a third wave of rec-ommender systems that considers thegoals and activities of users in additionto their preferences and needs. Thethird wave of recommender systemswill be capable of learning about a

cycles, making suggestions for healthypatterns of resting (such as Zeo, Fitbit).Personal workout recommenders maydisplay jogging route recommenda-tions (MapMyRun, Nike+, Strands, andothers).

Personal news sites recommend arti-cles from major news outlets or inde-pendent bloggers (such as HackerNews, Reddit, Digg, and others), whileemail systems help prioritize and filterour email (for example, Google gmailspam filtering and priority inbox). Per-sonal assistant software schedulesmeetings or finds reservations for arestaurant (for example, Siri; see figure5). Entertainment providers recom-mend movies and television programsto watch (such as Netflix).

Instead of providing yet anotherdomain-specific recommendation serv-ice, we foresee a third wave of recom-mender systems that will return to themetaphor of personal assistant (Negro-ponte 1995, Maes 1994). In thisarrangement, recommender systemsonce again persist with and act onbehalf of individual users to help themcope with the problem of information

overload across a range of domains. Asthe pace of data growth and consump-tion increases, the added dimensionsof cloud and mobile computing enablebetter personalized recommendationsthat can be provided in a larger varietyof relevant locations. Out of all of therecently started services, we see Siri(acquired by Apple) as coming closestto what the third-generation recom-mender systems will look like. In gen-eral, the third wave of recommendersystems will have these prominentdimensions: contextual awareness,cloud-based stores and computation,and emphasis on users’ long-termgoals.

Contextual Awareness New recommenders will use clues frommobile devices to generate contextuallyappropriate recommendations. Smart-phones provide a more convenient andpersistent basis for recommendations,as well as having access to new infor-mation, such as calendar, email, notifi-cations, and user GPS coordinates, thathelps drive timely and contextuallyappropriate suggestions. Additionaltechnologies such as eye tracking, ges-ture detection, or skin-tension meas-urement are expected to play a role inthe construction of such personal assis-tants, utilizing minimally invasive bio-metric feedback to characterize useraffect or emotional state, according torelevant research by Rosalind Picard(2000). The advanced features and pro-grammability of modern smartphonesmake them the perfect channel to drivethe third generation of recommendersystems.

Cloud-Based Stores and Computation The amount and diversity of data thatnew recommender systems will processis going to increase dramatically whilethe cost to process or store will decreasesignificantly. During the first twowaves, the IT investment required forlarge-scale recommendation systemswas prohibitive for many vendors topursue direct-to-consumer initiatives.However cloud computing hasreshaped the landscape. On-demandcomputational resources, such as Ama-zon EC2 or Rackspace, combined withdistributed-computing paradigms,

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Figure 5. A Screenshot from Siri.

This is an example of a third-wave recom-mender system and a precursor of themobile personal assistant revolution.

user’s contextual behaviors and prefer-ences to anticipate a user’s actions atany point in time and act upon them.

It is an exciting time for recom-mender systems. The currentconfluence of ever faster networks,massive adoption of mobile devices,the rise of cloud computing and socialtechnologies open a new window ofopportunity. Companies like Facebook,Google, and Apple are well positionedgiven their dominance in social media,search, and mobile technology to capi-talize on some of this opportunity. Butthe current industry scenario could notbe better for both researchers and prac-titioners who want to address newchallenges for recommender systems.There are ample opportunities for nim-ble entrepreneurs to connect data,algorithms, devices, and user needs tocreate solutions that carve out signifi-cant new market space.

References Amatriain, X.; Torrens, M.; Resnick, P.; andZanker, M., eds. 2010. Proceedings of theFourth ACM Conference on Recommender Sys-tems (RecSys ’10). New York: Association forComputing Machinery.

Bergman, L. D.; Tuzhilin, A.; Burke, R. D.;Felfernig, A.; and Schmidt-Thieme, L., eds.2009. Proceedings of the Third ACM Confer-ence on Recommender Systems (RecSys ’09).New York: Association for ComputingMachinery.

Cukier, K. 2010. Data, Data Everywhere: ASpecial Report on Managing Information.The Economist. 25 February.

Goldberg, D.; Nichols, D.; Oki, B. M.; andTerry, D. 1992. Using Collaborative Filteringto Weave an Information Tapestry. Commu-nications of the ACM 35(12): 61–70.

Jannach, D.; Zanker, M.; Felfernig, A.; andFriedrich, G. 2010. Recommender Systems: AnIntroduction. New York: Cambridge Universi-ty Press.

Kirn, W. 2010. My Cart, My Self. The NewYork Times Magazine. 30 December.

Konstan, J. A.; Riedl, J.; and Smyth, B., eds.2007. Proceedings of the 2007 ACM Conferenceon Recommender Systems (RecSys ’07). NewYork: Association for Computing Machin-ery.

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Item Collaborative Filtering. IEEE InternetComputing 7(1): 76–80.

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Picard, R. 2000. Affective Computing. Cam-bridge, MA: The MIT Press.

Pu, P.; Bridge, D. G.; Mobasher, B.; and Ric-ci, F., eds. 2008. Proceedings of the 2008 ACMConference on Recommender Systems (RecSys’08). New York: Association for ComputingMachinery.

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Resnick, P.; Iacovou, N.; Suchak, M.;Bergstrom, P.; and Riedl, J. 1994. Grou-pLens: an Open Architecture for Collabora-tive Filtering of Netnews. In Proceedings ofthe 1994 ACM Conference on Computer Sup-ported Cooperative Work, CSCW ’94, 175–186. New York: Association for ComputingMachinery.

Ricci, F.; Rokach, L.; Shapira, B.; and Kantor,P. B., eds. 2011. Recommender Systems Hand-book. Berlin: Springer.

Shardanand, U., and Maes, P. 1995. SocialInformation Filtering: Algorithms forAutomating “Word of Mouth.” In HumanFactors in Computing Systems CHI 95 Confer-ence Proceedings, 210–217. Reading, MA:Addison-Wesley.

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Töscher, A.; Jahrer, M.; and Bell, R. M. 2009.The BigChaos Solution to the Netflix GrandPrize. Technical Report, CommendoResearch & Consulting GmbH, Köflach,Austria.

Francisco J. Martin is a scientist entrepre-neur passionate about machine learningand distributed systems. He has cofoundedthree AI-related startups (Intelligent Soft-ware Components, SA, in 1999, Strands,Inc., in 2004, and BigML, Inc., in 2011) anddirectly raised more than $75 million inventure capital. He holds a five-year degreein computer science from Technical Univer-

sity of Valencia and received his Ph.D. (sum-ma cum laude) from Technical University ofCatalonia.

Justin Donaldson is cofounder and presi-dent of BigML, Inc., a web service for cloud-based machine learning of large sets of data.He has worked for six years on recom-mender systems at Strands.com, focusing onrelated analysis, interface, and visualizationmethods. He holds a B.S. in computer sci-ence from DePauw University, an M.S. inHCI, and a Ph.D. in informatics from Indi-ana University.

Adam Ashenfelter is a cofounder at BigML,Inc. He holds a B.S. and M.S. in computerscience from Oregon State University. Hespent seven years working on recommenda-tion systems, machine learning, and datafusion projects while at CleverSet, Inc., andStrands, Inc.

Marc Torrens received a MSc degree in com-puter science from the Universitat Politèc-nica de Catalunya (UPC), Barcelona, Catalo-nia, in 1997, and a PhD from the ÉcolePolytechnique Fédérale de Lausanne (EPFL),Lausanne, Switzerland, in 2002. Hecofounded Iconomic Systems SA in 1999and served the company as technology offi-cer. Iconomic Systems developed softwarefor planning travels on the Internet and wassuccessfully sold to i:FAO AG, Germany. In2004, Torrens cofounded Strands, Inc., inOregon, USA. Strands develops recommen-dation and personalization technologies tohelp people discover new things. He is cur-rently serving Strands as chief innovationofficer. Torrens has published more than 20referred papers on his work in internationalconferences and journals on artificial intel-ligence and usability.

Rick Hangartner is chief scientist at StrandsLabs, Inc., and an adjunct faculty memberat Oregon State University. He received hisBA and MS from the University of SouthFlorida and his PhD from Oregon State Uni-versity. His research focuses on statisticalinference, nonparametric data modeling,and adaptive systems.