Recommendation in the Social Web

Robin Burke and Jonathan GemmelWeb Intelligence Laboratory

DePaul UniversityChicago, IL, USA

Andreas Hotho and Robert JaschkeKnowledge and Data Engineering

University of KasselKassel, Germany

Abstract

Recommender systems are a means of personalizing the pre-sentation of information to ensure that users see the itemsmost relevant to them. The social web has added new di-mensions to the way people interact on the Internet, placingthe emphasis on user-generated content. Users in social net-works create photos, videos and other artifacts, collaboratewith other users, socialize with their friends and share theiropinions online. This outpouring of material has broughtincreased attention to recommender systems, as a means ofmanaging this vast universe of content. At the same time,the diversity and complexity of the data has meant new chal-lenges for researchers in recommendation. This article de-scribes the nature of recommendation research in social webapplications and provides some illustrative examples of cur-rent research directions and techniques.

It is difficult to overstate the impact of the social web. Thisnew breed of social applications is reshaping nearly everyhuman activity from the way people watch movies to howthey overthrow governments. Facebook allows its membersto maintain friendships whether they live next door or onanother continent. With Twitter, users from celebrities to or-dinary folks can launch their 140 character messages out toa diverse horde of “followers.” Flickr and YouTube usersupload their personal media to share with the world, whileWikipedia editors collaborate on the world’s largest ency-clopedia.

These varied applications all share some fundamentalcharacteristics. First, the focus is often partly (if not en-tirely) on the users themselves, and especially on their asso-cations and interactions with others. Second, many of theseapplications facilitate the organization and creation of onlinecontent. Finally, social applications often allow users to re-spond to posted content in a variety of ways, sometimes inreal-time, embedding each item in a rapidly evolving socialnetwork.

The complex information space generated by the socialweb offers a rich and dynamic environment for users to shareinformation, discover new content and meet new people.However, the success of the social web has made some ofthese benefits difficult to realize, due to the vast amount ofinformation available. Twitter has 50 million tweets per day;

Copyright c© 2011, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

which should you read? Every minute more than 24 hoursof video is uploaded to YouTube; what should you watch?Recommender systems have the potential to filter the oceansof data that make up the social web, and provide a personal-ized view for each user.

The types of recommender systems most commonly stud-ied by researchers and discussed elsewhere in this special is-sue are not always a great fit for the task of recommendationin the social web. Data used to recommend products in ane-commerce setting, for example, represent a user-item rela-tionship: a user might give a movie three stars, or a databasemight show which products a user has purchased. Much rec-ommender systems research has been focused on algorithmsappropriate to data in this form.

The data associated with the social web is radically multi-dimensional in comparison. To see why this is so, con-sider a social tagging system, such as the music sharing siteLast.fm or the citation sharing site BibSonomy. In such sites,users associate items with tags, short text labels in an uncon-strained vocabulary, and share these annotations with others.We can think of a tag as a kind of rating, a valuation thatthe user associates with an item. However, in contrast to nu-meric ratings, which allow direct comparison on a scale, tagsare textual and often idiosyncratic in their form. In addition,a user often applies multiple tags when annotating an item.So, in contrast to scalar ratings, a tagging system has valu-ations that are effectively vectors of tags, and the tag spacemay contain hundreds of thousands of unique terms.

Another way to view tags would be analogous to item de-scriptors such as might be found in a content-based recom-mender system. However, unlike an item catalog, the de-scriptors in tagging systems are not assigned by a central-ized entity using a standard vocabulary. They are created bya host of individuals for a variety of purposes, so a tag mustbe interpreted relative to an individual. For example, for userA, who is a car fancier, the tag “jaguar” might be similar touser B’s tag “xj-series”, whereas user C’s “jaguar” might bea label used only for jungle cats. Instead of a single one-dimensional description representing the content of an item,we have a family of them, each offering a different user’sperspective.

This article describes the landscape of the social web,sampling some of the components common across its manyapplications, and discusses some of the recommendation

tasks that naturally arise from this landscape. Taking so-cial tagging systems as the most well-studied application inthis area, we sample a few of the current techniques beingemployed to address its challenges.

Landscape of the Social WebThe social web can be considered as the aggregation of on-line interactions among users. These interactions can takemyriad forms, some of which are shown in Figure 1. Someinteractions are between individuals while others revolvearound online content or how that content is valued. In thissection, we examine some of the core entities found in thesocial web and their characteristics with respect to recom-mendation.

PeopleResearch on the World Wide Web often models that sys-tem as an information graph, with web pages linking to eachother in complex patterns. In this model, the authors do notparticipate and indeed, anonymity and difficulty in establish-ing authorship are two hallmarks of the web as we knowit. The social web, on the other hand, lends itself to graphsin which users are connected to each other, either directlythrough social links of various types or indirectly throughconnections to content.

The interactions between a user and his online socialcontacts can vary as dramatically as they do in real worldenvironments. Interactions can be directional, when theyare formed through subscription arrangements, as in Twit-ter, where the user being followed has no control over whochooses to be follower. They can also be bi-directional – asin the now famous “friend” relationship on Facebook. Inter-actions may involve groups of different sizes. Sometimes auser is posting content to a closed group of friends; some-times, to the whole world. These actions all require differentinterpretations.

Certain aspects of social links have been studied by re-searchers interested in applying concepts of social trust ingenerating recommendations (Victor, DeCock, and Cornelis2011). Some sites, such as epinions.com, allow users to ex-plicitly create links to others as individuals whose valuationsare reliable. There has been extensive research on the inte-gration of these social network considerations into conven-tional rating-based recommendation and many studies showthat having the right connections can be a very powerful aidin producing accurate recommendations.

Helping users build social connections is a basic recom-mendation operation in the social web. Friend recommen-dation, for example, is a common operation that can directlyleverage the social graph, as in Facebook’s built-in friendfinder that locates individuals with overlapping social cir-cles. Still, because of the nature of the social environment,user recommendation has multiple interpretations. A userseeking a recommendation might be looking for a friend orcoleague, but she might also be seeking a potential client,a romantic prospect, or a like-minded hobbyist, who couldbe more distant in the social network. In some applications,especially corporate intranets, the recommendation of indi-

viduals with particular skills or expertise may be an impor-tant task. Such an expert is unlikely to be found in the user’simmediate social circle.

ItemsThe social web presents a vast array of items that can be rec-ommended. One reason that people come to social web sitesis to hear about new items of the type that recommender sys-tems have typically presented: movies, music, restaurants.However, in the social web, there will often be multipleroutes into a given item. A music track might be accessedthrough the artist, through a tag, through an album, through afriend’s “favorites” list, etc. The multitude of links and inter-actions surrounding an item provides additional clues aboutits meaning and significance, but also poses a challenge forthe recommender.

In addition to exogenous media items, a social web sitewill also contain user-generated content, which is also fairgame for recommendation. User-generated items, such asblog entries, status updates, and other data, have the bene-fit of being closely embedded in the networks of interactionthat a social web site hosts. Often, however, they are not in-tended to stand alone and can sometimes only be interpretedin context. For example, the Twitter post about “Who’sAfraid of Virginia Woolfe?” in Figure 1 makes much moresense if you know that the poster is a college professor, likethe male lead in the play.

Recommendations of user-generated content must alsotake context into account. Consider a user who just viewedphotographs annotated with “wedding” and “New York”.Would such a user be interested in seeing all photos withthese tags? Most likely, the user interest is related to the par-ticular individuals getting married, and pictures of strangers’New York weddings would not be welcome recommenda-tions. On the other hand, if we know that the user is a pro-fessional photographer, that context would give rise to anentirely different set of recommendations.

ValuationsUsers in social web sites can express their opinions aboutcontents of the site in a variety of ways. They can tag,post, tweet, rate, or write reviews. These reactions canbe read, responded to, or forwarded to others. Substantialvaluations like reviews may become items of recommenda-tion in their own right. Many users prefer a peer review toa professionally-authored one, especially if the author hassome commonality with themselves.

In social annotation systems, a common application ofrecommendation is to assist users in labeling items throughthe recommendation of tags. If properly implemented, tagrecommendation reduces the cognitive load of tagging, en-couraging users to do more of it, and also reduces theamount of noise in the tag vocabulary by reducing redun-dancies like “NewYork”, “New-York”, “New York”, etc.1

1 Interestingly, tag recommendation is somewhat controversialamong those concentrating on the ontological aspects of tagging,since an unbiased consensus requires that valuations be made inde-pendently between users and the recommender defeats user inde-

Figure 1: Landscape of the Social Web: A user has many options on how to with connect other users, what types of content toconsume, and how to respond to this content.

Recommendation TasksThe complexity of recommendation in the social web is notsolely a function of the variety of possible items to be rec-ommended. Its multi-dimensional nature means that a widevariety of recommendation tasks and modalities can be sup-ported.

We can distinguish recommendation tasks by their inputand output, and by the semantics of the recommendation op-eration. The wide variety of outputs has already been noted.The essential input to any recommender system is the profileof the user for whom the recommendation is sought. How-ever, in the social web, user profile information may be aug-mented in various ways. For example, tag recommendationrequires information about the resource to which the tag willbe applied.

Because of the multi-dimensional nature of the supportingdata, the social web supports a wide variety of recommen-

pendence.

dation semantics. For example, in recommending items, thesystem can present items personalized in terms of past view-ing behavior, in terms of the behavior of friends, or in termsof a broader group of peers with similar interests. Thesewould be presented to and interpreted by the user in differ-ent ways.

Adomavicius and Tuzhilin defined the recommendationproblem as that of predicting items with the highest utilityfor a given user (Adomavicius and Tuzhilin 2005). This canbe achieved by a function that computes the utility for a user-item pair u(c, i) where c is a user and i is an item. In thesocial web, we extend this formulation in three ways. First,by augmenting the input to include requirements related therecommendation. For example, when recommending tagsin a social tagging system, we are interested in the utilityof a tag relative to a user-resource combination: “What tagwould this user want to assign to this resource?” So, theutility needs to be a function of the whole triple: user, tag,

and item. Second, we note that in the social web, recom-menders are not restricted to a user/item dichotomy, but mayrecommend users, tags, reviews, and many other things, sothe object of recommendation must be broadened to includeanything in the system. Finally, we see that in the social weba variety of types of utility may be relevant, as in the case ofuser recommendation, which can include friends, businesscontacts, or professional experts. So, a recommender sys-tem for a social web application may need a family of func-tions ux(c, r, o) where r is some set of requirements on topof the user profile, o can be any object contained in the ap-plication, and x stands for different types of utility that therecommendations need to satisfy.

Techniques for RecommendationGiven the diversity of avenues and needs for recommen-

dation in the social web, it is not surprising that many dif-ferent techniques have been employed. The kind of recom-mendation, the motivation of the user, the type of underlyingitem, the form of valuation and the mode of user interactionall impact the quality of the data as well as the relative per-formance of the recommender. Research has shown that, inspite of the complexity, sparsity and inherent noisiness ofmany of the dimensions of social web data, recommendersgenerally do better when they take advantages of more ofthese dimensions rather than fewer. This is in contrast tosome results with rating-based datasets that have found littlebenefit to adding integrating content and collaborative datain some cases (Pilaszy and Tikk 2009).

The preceding discussion will have given some sense ofthe breadth of research in social web recommendation andthe variety of problems addressed. In the remainder of thisarticle, we will look in more detail at the problem of recom-mendation in social tagging systems. This is the most de-veloped area of research in social web recommendation, andwe will look at three different approaches to the problem ofintegrating the multiple dimensions (users, tags, items) ofthe data.

GraphsSince the multi-dimensionality of the social web is reflectedin the graph it forms, one can apply graph-based algorithmsfor recommendation to directly exploit the relationships be-tween the entities, e. g., between users, items, and valua-tions (cf. Fig. 2). The break-through in web search at theend of the 90’s was founded on a graph-based method: thePageRank algorithm (Brin and Page 1998). PageRank re-flects the idea that a web page is important if there are manypages linking to it, and if those pages are important them-selves. It is natural to apply similar methods for recommen-dation in the social web. The key idea of the FolkRank algo-rithm (Benz et al. 2010) is that an item which is tagged withimportant tags by important users becomes important itself.The same holds, symmetrically, for tags and users. We havethus a graph of vertices which are mutually reinforcing byspreading their weights.

Because of the different nature of folksonomies comparedto the web graph (undirected triadic hyperedges instead of

The ECML PKDD Discovery Challenge 2009In 2009, the Knowledge and Data Engineering group at theUniversity of Kassel organized the second ECML PKDDDiscovery Challenge. It focused on the task of recommend-ing tags in the social bookmark and publication sharing sys-tem BibSonomy (www.bibsonomy.org). Three tag recom-mendation tasks were formulated, based on the experiencegained in the 2008 challenge.

• The first task was a cold-start task which included userspreviously unknown to the system, new items, and some-times new items and new users together.

• The second task concentrated on a more conventional sce-nario where something is known about the user and item.

• The third challenge was online. Participants were invitedto connect their recommendation engines to BibSonomyfor live provision of results to its users. This challengeintroduced the issue of response time in addition to rec-ommendation quality.

In order to enable the competition, the tag recommenda-tion feature of BibSonomy was adapted to use a multiplexer,which distributed requests to all connected recommenda-tion engines. When a user edited a post, all recommendersgot the same requests and had to provide an answer withinone second. The request and all answers were stored in adatabase for evaluation purposes. One recommender wasrandomly selected and the result was presented to the user.The system did not only store the post and the chosen tagsbut allowed for monitoring the click behavior of the user.If the user was not satisfied with the recommended tags hecould ask for another set.The results of the challenge were very encouraging. Mostof the recommenders delivered their recommendations intime even if they were connected via the Internet. But somesystems that produced high-quality recommendations in off-line challenges struggled to meet the real time conditions ofthe online setting.In all, 21 research groups participated in the challenge andover 150 users downloaded the datasets. For the online task,ten participants from seven countries came up with work-ing solutions. Thirteen recommendation components werefielded for a five week period. The online competition wasa unique and exiting experience for the participants and forBibSonomy users, and the winning recommendation solu-tion is still in place providing recommendations in the sys-tem.

Eisterlehner, F.; Hotho, A.; and Jaschke, R., eds. 2009.ECML PKDD Discovery Challenge 2009 (DC09), volume497 of CEUR-WS.org.

Jaschke, R. 2011. Formal Concept Analysis and Tag Recom-mendations in Collaborative Tagging Systems, volume 332of Dissertationen zur Kunstlichen Intelligenz. Heidelberg,Germany: Akademische Verlagsgesellschaft AKA.

semanticfirefox

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Figure 2: Excerpt of a folksonomy hypergraph from a socialbookmarking system. Each assignment of a tag to an itemthat a user performs is represented by a hyperedge in thistripartite graph of users, tags and items. The example showsthree posts made by two users.

directed binary edges), PageRank cannot be applied directlyon folksonomies. This problem is overcome in two steps.First, we transform the hypergraph into an undirected graph.Then we apply a differential ranking approach that dealswith the skewed structure of the network and the undirect-edness of folksonomies, and which allows for topic-specificrankings.

Folksonomy-Adapted PageRank First we convert thefolksonomy hypergraph F = (U, T, I, Y ) into an undirectedtri-partite graph GF = (V,E). The set V of nodes of thegraph consists of the disjoint union of the sets of tags, usersand items (i. e., V = U ∪T ∪I). All co-occurrences of tagsand users, users and items, tags and items become edges be-tween the respective nodes. I. e., each triple (u, t, i) in Ygives rise to the three undirected edges {u, t}, {u, i}, and{t, i} in E.

Like PageRank, we employ the random surfer model, thatis based on the idea that an idealized random web surfernormally follows links (e. g., from an item page to a tag ora user page), but from time to time jumps to a new nodewithout following a link (Brin and Page 1998). This resultsin the following definition.

The rank of the vertices of the graph is computed (like inPageRank) with the weight spreading computation

~wτ+1 ← dAT ~wτ + (1− d)~p , (1)

where ~w is a weight vector with one entry for each node inV , A is the row-stochastic version of the adjacency matrix2

of the graph GF defined above, ~p is the random surfer vec-tor – which we use as preference vector in our setting, andd ∈ [0, 1] is determining the strength of the influence of ~p.By normalization of the vector ~p, we enforce the equality

2aij := 1degree(i)

if {i, j} ∈ E and 0 else

||~w||1 = ||~p||1. This3 ensures that the weight in the systemwill remain constant. The rank of each node is its value inthe limit ~w := limτ→∞ ~wτ of the iteration process.

For a global ranking, one will choose ~p = 1, i. e., the vec-tor composed by 1’s. In order to generate recommendations,however, ~p can be tuned by giving a higher weight to certainnodes. For a tag recommendation we increase the weight forthe user node u and the item node i. The recommended tagsare then the top tag nodes in the ranking.

As the graph GF is undirected, most of the weight thatwent through an edge at step τ will flow back at τ + 1. Theresults are thus rather similar (but not identical, due to therandom surfer) to a ranking that is simply based on edge de-grees. The experiments we performed showed that the topic-specific rankings are biased by the global graph structure.As a consequence, we developed the following differentialapproach.

FolkRank – Topic-Specific Ranking The undirectednessof the graph GF makes it very difficult for other nodes thanthose with high edge degree to become highly ranked, nomatter what the preference vector is. This problem is solvedby the differential approach in FolkRank, which computes atopic-specific ranking of the elements in a folksonomy. Inour case, the topic is determined by the user/item pair (u, i)for which we intend to compute the tag recommendation.

1. Let ~w(0) be the fixed point from Equation (1) with ~p = 1.

2. Let ~w(1) be the fixed point from Equation (1) with ~p = 1,but ~p[u] = 1 + |U | and ~p[i] = 1 + |I|.

3. ~w := ~w(1) − ~w(0) is the final weight vector.

Thus, the difference between the two fixed points are de-termined. Much of the network is unaffected by the extraemphasis placed on the nodes for u and i, and so the dif-ferential approach removes these neutral nodes and allowsthe algorithm to concentrate on those that receive weight byvirtue of their association with u and i. We call the resultingweight ~w[x] of an element x of the folksonomy the FolkRankof x.

Multi-mode Recommendations For generating tag rec-ommendations for a given user/item pair (u, i), we computethe ranking as described and then restrict the result set to thetop tag nodes. Similarly, one can compute recommendationsfor users (or items) by giving preference to certain users (oritems). Since FolkRank computes a ranking on all three di-mensions of the folksonomy, this produces the most relevanttags, users, and items for the given users (or items).

Remarks on Complexity One iteration of the adaptedPageRank requires the computation of dA~w + (d − 1)~p,with A ∈ Rs×s where s := |U | + |T | + |R|. If τ marksthe number of iterations, the complexity would therefore be(s2 + s)τ ∈ O(s2τ). However, since A is sparse, it is moreefficient to go linearly over all tag assignments in Y to com-pute the product A~w. After rank computation we have tosort the weights of the tags to collect the top tags.

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FolkRankmost popular tags by resource

most popular tags by usermost popular tags

Figure 3: Recall and precision for an increasing number ofrecommended tags on a 2005 Delicious post-core at level 10.

Results We evaluated the performance of FolkRankagainst other baseline methods on a dataset from Deli-cious (Jaschke et al. 2008). The precision-recall plot inFigure 3 shows how the recall increases, when more tags ofthe recommendation are used. Simultaneously, the precisiondrops. The plot reveals the quality of the recommendationsgiven by FolkRank compared to other baseline approaches.The top ten tags given by FolkRank contained on average80 % of the tags the users decided to attach to the selecteditem. For its top recommendations, FolkRank reaches a pre-cision of 58.7 %.

FactorizationThe multi-dimensional data of the social web can also berepresented in the form of a tensor that can be factorized toexploit the latent semantic structure between users, items,and valuations. Rendle et al. introduced a model to learn thefactorization of the folksonomy tensor for tag recommen-dation (Rendle et al. 2009). This new model focuses on aspecific error function that is better suitable to predict tagsin a personalized way.

Factorization Model Given a subset S of Y that repre-sents the training data, the goal is to learn a predictor Y thatpredicts the tag assignments from Y that are not known inS. Therefore, the tensor Y is estimated by the three matri-ces U ∈ R|U |×kU , I ∈ R|I|×kI , T ∈ R|T |×kT and the coretensor C ∈ RkU×kI×kT . The factorization of the predictorY can then be expressed as follows:

Y := C ×u U ×i I ×t T (2)

The low-rank feature matrices represent the correspondingusers, items, and tags resp. by a small number of latent di-mensions kU , kI , and kT . The core tensor C contains theconnections between the latent factors. After the parameters

3. . . together with the condition that there are no rank sinks –which holds trivially in the undirected graph GF.

C, U , I, T have been learned,

yu,i,t =∑u

∑i

∑t

cu,i,t · uu,u · ii,i · tt,t (3)

predicts how well the tag t fits for the given user/item pairu, i.

Learning the Model The model parameters C, U , I, Tcan be learned by optimizing some quality criterion thatcompares Y with Y . Symeonidis et al. (Symeonidis,Nanopoulos, and Manolopoulos 2008) proposed to factorizeY through minimizing the element-wise loss on the elementsof Y by optimizing the square loss, i. e.,

argminC,U,I,T

∑(u,i,t)∈U×I×T

(yu,i,t − yu,i,t)2

This resembles higher order SVD (HOSVD), the multi-dimensional analog of singular value decomposition (SVD)for tensors. See (Kolda and Bader 2009) for a recent survey.

Rendle et al. (Rendle et al. 2009), on the other hand, pro-pose ranking with tensor factorization, a method for learn-ing an optimal factorization of Y for the specific problem oftag recommendations. Therefore, the observed tag assign-ments for a post (u, i) ∈ PS are divided into positive (T+

u,i),negative (T−u,i), and missing values. Only the positive andnegative values are used in the optimization:

argmaxC,U,I,T

∑(u,i)∈PS

1

|T+u,i||T

−u,i|

∑t+∈T+

u,i

∑t−∈T−

u,i

1

H(u, i, t+, t−)

with H(u, i, t+, t−) = 1 + eyu,i,t−−yu,i,t+ . The optimiza-tion is performed using gradient descent with a stochasticupdate approach.

Results Figure 4 shows published results for the rankingwith tensor factorization (RTF) technique on a dataset fromBibSonomy. As the graph shows, as more latent factors areincluded in the RTF model (RTF 8-128), F1 (the harmonicmean of recall and precision) increases and at 128 factors,the performance is comparable to FolkRank on this tag rec-ommendation task. The importance of optimizing only overthe positive and negative observations is demonstrated bythe performance of the HOSVD method, which is signifi-cantly poorer. The advantage of Rendle’s approach is thatone can control the tradeoff between speed and quality byselecting an appropriate number of dimensions.

HybridsHybrid recommenders integrate the results of severalcomponent recommenders into a single recommendationset (Burke 2002). They have been shown effective in tra-ditional e-commerce settings, with similar performance toother integrative solutions. In the context of the social web,hybrids have been applied most notably to social annotationsystems, especially tag and item recommendation. (Gem-mell et al. 2010a; Gemmell et al. 2010b)

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Figure 4: The F1-Measure for an increasing number of rec-ommended tags on a BibSonomy dataset.

Algorithm As one example, consider the linear weightedhybrid described in (Gemmell et al. 2010b). A schematicof this design is shown in Figure 5: as shown, a numberof different recommendation components work together toassign a recommendation score combining their individualvalues with a linear weight or α. The weights are learnedthrough random-restart hill-climbing.

Figure 5: The linear weighted hybrid takes a user and itemas input and passes it along to each component. The com-ponents individually produce relevance scores which the hy-brid aggregates into a final result based on the α values.

The recommendation task in this case is to find interest-ing items within a social tagging system, knowing only theuser and her prior tagging behavior. The aim of the hybrid isto achieve good performance from components that are in-dividually simple but leverage different aspects of the data.For example, we can build a two-dimensional recommenda-tion algorithm by ignoring the tag dimension of the data andlooking just for users who have tagged similar items. Or wecan ignore the items and look only for neighbors with sim-ilar tag usage. Even though each of these algorithms inde-pendently might not be terribly effective, results show that

combined in a linear hybrid they can achieve performancecomparable to more complex algorithms.

Results Figure 6 presents an example of results (Gemmellet al. 2010b) using data from CiteULike4, a social tag-ging and publication sharing site, similar to BibSonomy. Inthis experiment, the hybrid combines six constituent compo-nents:

Pop ignores the user and merely returns a score based onthe popularity of the item.

TagSim treats both the user and item as a vector of tags andcomputes the cosine similarity between the two.

KNNur and KNNut employ user-based collaborative filter-ing modeling users as either items or as tags.

KNNru and KNNrt model items either as users or as tagsin item-based collaborative filtering components.

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����� ����� ������ ������ ������������ �� ������ ��������� �������� ��������� �������� ������Figure 6: The recall (x-axis) and precision (y-axis) plottedfor item recommendations sets of size one through ten.

The success of the hybrid here was achieved by leverag-ing all of the components, not just the strongest performingindividuals, into an integrative model that exploits comple-mentary dimensions of the data. This can be seen by exam-ining the weights that were learned in the course of buildingthe hybrid, shown in Table 1.

Pop TagSim KNNur KNNut KNNru KNNrt0.217 0.184 0.270 0.025 0.162 0.142

Table 1: Contribution of the individual components.

All of the components contributed to the hybrid, regard-less of how well they performed alone. KNNur for exam-ple had an α of 0.270, the largest individual contribution.KNNut in contrast had an α of 0.025, perhaps because itoffered little information that the other user-based collab-orative filtering method did not already contribute. Theremaining recommenders offered significant contributions,underscoring the need to leverage multiple dimensions of

4http://www.citeulike.org/

the data. Another interesting case is that of the popularitybased recommender. Alone it was the worst performing rec-ommender. As a component it had a relatively large α of0.217, meaning it accounted for more the 20 % of the hy-brid’s final score.

Discussion The weighted hybrid is a simple, extensible,and flexible approach to the problem of integrating the mul-tiple dimensions of social web data, and its accuracy intag and item recommendation is comparable to other state-of-the-art algorithms. The convergence time for the alphaweights is dependent on the number of algorithms beingcombined, so it is essential to assemble a diverse, but not toolarge, collection of components. As seen in our example,diversity may be more important than raw accuracy sincepoorly performing algorithm can become a strong contribu-tor.

ConclusionThe social web is an important emerging area in recom-mender systems research. Compared to recommendationsproblems typically found in e-commerce settings, the socialweb presents unique challenges. In particular:• The diversity of the items to which recommendation may

be applied, even within a single application,• The wide range of recommendation semantics that prevail

depending on the nature of the application, and• The complexity and multi-dimensional nature of the so-

cial web data to which recommendation algorithms areapplied.For these reasons, the choice of recommendation ap-

proach is likely to be highly dependent on the specific rec-ommendation task and domain. Social tagging is the best-studied social web recommendation application, and in thisarticle, we have illustrated three different approaches to thisproblem.

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