Hugo LiuMIT Media Laboratory

20 Ames St., Cambridge, MA, USA

hugo@media.mit.edu

Pattie MaesMIT Media Laboratory

20 Ames St., Cambridge, MA, USA

pattie@media.mit.edu

ABSTRACTRecommender systems generally construct models of people which are specific to a particular application domain. With the emergence of social network software on the World Wide Web – where self-descriptive personal profiles are represented by a set of keywords and phrases spanning a broad range of a person’s interests and activities – there is an opportunity to construct a much more domain generic model of a person, which we sug-gest can operate at the granularity of a person’s identities and tastes.

Taking the social data mining approach to person modeling and recommendation, we used 100,000 personal profiles mined from online social networks to build an InterestMap. Put simply, it is a giant, dense connectionist-semantic network whose nodes are keywords and phrases spanning dually 1) the broad space of per -sonal interests (i.e. hobbies, sports, music, books, television shows, movies, cuisines), and 2) the space of cultural identities (e.g. “raver,” “dog lover,” “fashionista”); and whose edges re-veal the apparent strength of relatedness of two nodes. Once a novel profile is situated on the InterestMap as some pattern of node activations, the system can infer aspects of a person’s iden-tities and tastes, as well as make recommendations for new in-terests.

In this paper, we examine the suitability of social network pro-files to the task of identity and taste-based recommendation. By contrasting InterestMap’s “network” style recommendation mechanism against collaborative filtering systems, we expose their differing implications for transparency and trust, data source anonymity, and temporality. We explain the technical nexus of how InterestMap is constructed, and evaluate the sys-tem’s performance in a recommendation task.

Categories and Subject Descriptors…

General Terms…

KeywordsUser modeling, recommender systems, cultural identity, social networks, collaborative filtering

1. INTRODUCTION

The subgenre of artificial intelligence research known as recom-mender systems (cf. Resnick & Varian, 1997) have thus far en-joyed remarkable practical and commercial success. One of the most prevalent techniques for recommendation, collaborative filtering (Shardanand & Maes, 1995), powers product recom-mendations on e-commerce sites like Amazon1 and Ebay2, and a related link-based rating technology called PageRank™ (Page, Brin, Motwani & Winograd, 1998) provides the technical foun-dation which makes search engines like Google3 possible. Rec-ommender systems have also been deployed and researched for a variety of subject domains, including movie recommendation (Miller et al., 2003), and research paper recommendation (Mc-Nee et al., 2002).

Typically, the user models being constructed in these systems are specific to a single subject domain or application. As such, user models must often be built anew for each new recom-mender domain. In addition, there is little coordination amongst the different single-domain user models, and no cross-domain recommendation capability. One proposed approach to integrat-ing recommendations from different domains into a single meta-recommendation system (Schafer, Konstan & Riedl, 2002) calls for explicit user control of the integration parameters.

In this paper, we investigate a different approach to integration. Employing a social data mining approach (Terveen & Hill, 2001) to recommendation, we harvest rather domain-generic profiles of personal interests and identities from web-based so-cial networks (boyd, 2004) such as Friendster4, Orkut5, and The-facebook6. Analyzing patterns of how interests and cultural identities co-occur over this corpus, we constructed a “map” of the interrelatedness of interests and cultural identities called an InterestMap, which can itself power cross-domain (interest do-main, that is) recommendations, or alternatively provide contex-tual-support to domain-specific recommenders by situating a person in a larger cultural and aesthetic space.

The rest of this paper is structured as follows. First, we com -pare InterestMap’s “network” style representation and recom-mendation mechanism against the collaborative filtering ap-proach and reveal their differing implications for transparency and trust, data source anonymity, and temporality. Second, we discuss the ins and outs of self-descriptive social network pro-files – their fidelity, how identity and tastes can be inferred from them, and how they can facilitate recommendation. Third, we present the technical nexus of InterestMap – how profiles were mined and normalized, and the map was constructed taking an information theoretic approach. Fourth, we evaluate perfor-mance of InterestMap in a recommendation task using five-fold cross validation. We conclude by summarizing our contribution and giving directions for future research.

2. THE INTERESTMAP APPROACHThe approach taken by InterestMap is to 1) mine social network profiles; 2) extract out a normalized representation by mapping casually-stated keywords and phrases into a formal ontology (e.g. “Nietzsche” “Friedrich Nietzsche”, “dogs” appearing under the “passions” category “Dog Lover”); 3) augment the normalized profile with metadata (e.g. “War and Peace” also causes “Leo Tolstoy,” “Classical Literature,” and other metadata to be included in the profile, at a discounted value of 0.5 for ex-ample) to facilitate connection-making; and 4) apply an infor-mation-theoretic machine learning technique called pointwise mutual information (Church et al., 1991), or PMI, over the cor-pus of profiles to learn the semantic relatedness of every possi -ble pair of person features (interests or identities). What results is a giant dense semantic network whose nodes are person fea-tures and edges are the semantic connectedness weights calcu-

1 http://www.amazon.com2 http://www.ebay.com3 http://www.google.com4 http://www.friendster.com5 http://www.orkut.com6 http://www.thefacebook.com

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lated by the PMI algorithm. Figure 1 is a visualization of a sim-plified InterestMap.

Figure 1. A screenshot of an interactive visualization program, running over a simplified version of InterestMap (weak edges are discarded, and edge strengths are omitted). The “who am i?” node is an indexical around which a person is constructed. As interests are attached to the indexical, other interests and identity-descriptors are also pulled into the visual neighborhood.

An InterestMap can be applied in a simple manner to accom-plish several tasks, such as identity recognition, interest recom-mendation, and person recommendation. Given a seed profile which represents a new user, the profile is normalized and the interests and identity-descriptors contained therein are mapped onto the nodes of the InterestMap, leading to a certain activation of the network. By spreading activation (Collins & Loftus, 1975) outward from these seed nodes, a surrounding neighbor-hood of nodes which are connected strongly to the seed nodes emerges. Coarsely speaking, these constitute interest recom-mendations because the immediate neighborhood describes in-terests which are proximal (and thus relevant) to this distributed representation of a person. A subset of the resulting semantic neighborhood of nodes will be identity-descriptor nodes, and the most proximal and strongly activated of these can be thought of as recognized identities. It is also possible to recommend other people to a user based on maximal overlap of interests and iden-tities. To calculate the affinity between two people, two seed profiles lead to two sets of network activations, and the strength of the contextual overlap between these two activations can be used as a coarse measure of affinity.

Compared with collaborative filtering, our approach bears dif-ferent implications for transparency and trust, data source anonymity, and temporality, and these are discussed below.

2.1 Transparency and TrustThat a user trusts the recommendations served to him by a rec-ommender system is important if the recommender is to be use-ful and adopted. Among the different facilitators of trust, Wheeless & Grotz (1977) identify transparency as a prominent

desirable property. When a human or system agent discloses its assumptions and reasoning process, the recipient of the recom-mendation is likely to feel less apprehensive toward the agent and recommendation. Also in the spirit of transparency, Her-locker et al. (2000) report experimental evidence to suggest that recommenders which provide explanations of its workings expe-rience a great user acceptance rate than otherwise.

Compared to a collaborative filtering (CF) recommender, Inter-estMap may hold two advantages in the operationalization of transparency: the communicative value of features, and the com-munication of group tendencies. Together, they make the rec-ommendation rationale more intelligible and errors easier to ra-tionalize and forgive.

First, because a CF recommender’s vector of feature ratings generally result from a trace of a user’s interaction with an ap-plication, the individual features may individually be arbitrary, having low communicative value, that is to say, a user is not in-tending to communicate any of his interactions with an applica-tion as recommendations for another user. This is virtue in cer-tain respects because it represents a capture of more realistic be-havior, the user not consciously manipulating recommendations. However, in the case of an erroneous recommendation of an ar-bitrary obscure feature, recommendation recipients may find it even harder to justify or rationalize the purpose for the recom-mendation than if the erroneous feature had been at least promi-nent or important in some sense. For example it might be easier to rationalize or forgive the erroneous recommendation of a more prominent feature like L.v. Beethoven’s Symphony No. 5 to a non-classical-music-lover than an equally erroneous recom-mendation of a more obscure or arbitrary feature like Max Bruch’s Op. 28.

In contrast to CF, InterestMap’s feature-set has arguably more communicative value because they originate from social net-work self-descriptive personal profiles. Donath & boyd (2004) report that a person’s presentation of self in profiles is in fact a strategic communication and social signalling game. Interests chosen for display are not just any subset of possessed interests, but rather, are non-arbitrary interests meant to be representative of the self, and consciously intended to be communicated so-cially to those reading the profile. Users may be more likely to rationalize or forgive mis-recommendation of non-arbitrary fea-tures than otherwise; after all, human recommenders may mis-recommend, but rarely mis-recommend completely arbitrary things.

Second, InterestMap does not recommend by citing individual user’s vector of ratings, but rather, recommends using a connec-tionist-semantic network which represents the tendencies of a large group of people. Being able to visualize a group’s topol-ogy as in Figure 1 may hold particular advantage over CF in ac-cessibility and intelligibility because a user may be more likely to regard connections which are proven important to the group as a better justification in the case of erroneous recommenda-tion, than connections arising through the behaviors of particular individuals. For example, in Figure 1, it is plain to see that “Sonny Rollins” and “Brian Eno” are each straddling two differ-ent cliques of different musical genres, and we suggest that this kind of intelligibility can assist a user in rationalizing a recom -mendation. Committing an error that another human would

make is more likely to garner sympathy than a completely inhu-man error.

2.2 Data Source AnonymityWe have proposed the mining of social networks and are inter-ested in building a domain-generic recommender resource which can be publicly distributed and used to power other domain-spe-cific recommender systems. However the nature of the data source makes anonymity a particularly sensitive issue. An early phase of the InterestMap construction process is to normalize the casually-stated keywords and phrases into a formal ontology (e.g. “Nietzsche” “Friedrich Nietzsche”, “dogs” appearing under the “passions” category “Dog Lover”). This means al-ready that the unrestricted idiosyncratic language which bears traces of an authorship are beginning to be wiped away. Gener -ally, CF systems already start with features and ratings which conform to some formal ontology. Unfortunately, this is not a guarantee of anonymity, only pseudonymity. A user’s name is simply wiped away and replaced with a unique id, but the pro-file’s integrity is intact. Because the number of features is quite large in our space of interests and identities, it may be possible to guess the identity of some nameless profiles whose constitu-tions are quite unique, or at least, it lends itself to the perception that privacy of the source data may be violated.

Rather than preserving individual profiles, InterestMap simply uses these profiles to learn the strengths of connections on a net-work whose nodes already exist (they are simply an exhaus-tively enumeration of all features in the ontology). The final structure assures the anonymity of the data source, and is much easier to distribute. Additionally, it is possible to use varied data sources whose feature-sets are slightly different (for exam-ple, one social network is missing “cuisines,” another is missing “sports”) because the resulting InterestMap hides such differ-ences.

2.3 TemporalityFinally, there is the issue of temporality – keeping the recom-mender knowledge base current and up-to-date. This is an issue where collaborative filtering easily wins out over our “map” ap-proach. Because CF recommenders keep each individual user’s rating history separate, any change made to an individual user’s model can automatically be understood by the CF recommenda-tion algorithm. However, InterestMap’s collapse of individual models onto a communal map does not allow for such a simple update. The whole of the map must be retrained from a new im -age of the source data. Computationally, this is quite expensive and so the map approach is less suitable for domains in which the feature space is changing rapidly, e.g. news stories. Fortu-nately, the space of interests and identities does not evolve with such rapidity, tending more to move at the pace of culture, and so requires less frequent sampling of social network profiles to stay up-to-date.

3. SELF-DESCRIPTIVE PERSONAL PROFILES IN SOCIAL NETWORKSThe recent emergence and popularity of web-based social net-work software (boyd, 2004; Donath & boyd, 2004) such as Friendster, Orkut, and Thefacebook can be seen as a tremendous source of domain-independent user models, which might be more appropriately termed, person models to reflect their gener-

ality. To be sure, well over a million self-descriptive personal profiles are available across different web-based social net-works. While each social network has an idiosyncratic repre-sentation, the common denominator across all the major web-based social networks we have examined is the representation of a person’s broad interests (e.g. hobbies, sports, music, books, television shows, movies, and cuisines) as a set of keywords and phrases.

In addition, more than just interests, higher-level features about a person such as cultural identities (e.g. “raver,” “extreme sports,” “goth,” “dog lover,” “fashionista”) are also articulated via a category of special interests variously named, “interests,” “hobbies & interests,” or “passions.” In the web page layout of the personal profile display, this special category of interest al -ways appears above the more specific interest categories, en-couraging a different conceptualization of these interests; they are potentially interests more central to one’s own self-concept and self-identification. Of course, syntactic and semantic re-quirements are not enforced regarding what can and cannot be said within any of these profile entry interfaces, but based on our own experiences, with the exception of those who are inten-tionally tongue-and-cheek, the special interests category is usu-ally populated with descriptors more central to the self than other categories. For example, a person may list “Nietzsche” and “Neruda” under the “books” category, and “reading,” “books,” or “literature” under the special interests category. In the normalization of profiles, identity descriptors are inferred from descriptors listed under the special interests category (e.g. “dogs” “Dog Lover,” “reading” “Book Lover”, “decon-struction” “Intellectual”).

The remainder of this section exposes some of the major advan-tages and disadvantages of applying this corpus of personal pro-files to the recommendation problem; in addition, we further evaluate our paper claim that recommendations made with this corpus are indeed be driven by identity and taste.

3.1 AdvantagesThere are several advantages to applying this corpus of personal profiles to the recommendation problem. First, as was discussed earlier in Section 2.1, the interest features given in the corpus are demonstrated to have communicative value and are non-ar-bitrary by virtue of a person consciously including the interest in the profile; this represents a marked advantage over history-of-interactions data gathered within a particular application, which is not hand-crafted.

Second, as users of social network are already motivated to maintain their profiles in the context of that end-user application for purposes such as grooming social relationships and finding dates (although boyd counters that some profiles are rarely up-dated), this corpus is likely to stay fresh and current and can be re-sampled periodically to update the recommender.

Third, unlike some other genres of “personal profiles” whose features are chosen from existing lists, such as those used for online marketing (e.g. “beauty,” “technology,” “sports,” etc.), social network profiles are entered as free text, posing few re-strictions on what can be said and allowing a rich feature space to power recommendation; it is up to our system to opportunisti -cally recognize those free text fragments and map them into for -mal ontologies.

Fourth, rather than representing identity using small demo-graphic buckets (e.g. age 18-30; income: above $100,000), our system infers a broader set of psychographic buckets (e.g. “Dog Lover,” “Book Lover”, “Intellectual”) from descriptors listed under the special interests category. Arguably, psychographic identity features are more relevant and predictive of good rec-ommendations than demographic ones; at least, this is the cur-rent thinking in the marketing literature (Zaltman, 2003).

3.2 DisadvantagesIn boyd’s (2004) analysis of the Friendster social network, ques-tions the accuracy and fidelity of profiles. Because social net -works collapse social relations from a variety of contexts (e.g. work, high school, family) down into a singular list of friends, and because social networks like Friendster have a dual use as a dating site, there is a tremendous politics to the presentation of self. One must craft a profile which can at once attract potential suitors, yet not be perceived as false by friends. It is also neces -sary to negotiate the presented persona so as to be acceptable by friends from a variety of social contexts; as a result, boyd sug-gests that users are particularly apprehensive of including inter-ests which might possibly perceived as faux pas’s (how many hipsters secretly listen to Britney Spears but refuse to report it?). Because of these concerns, profiles tend to represent a person from a limited or skewed perspective.

There is also concern that the space of interests being reported through social network profiles is too limited to a few popular interests. This would seemingly preclude many potentially suc-cessful recommendations from being made, but are for whatever reason so rarely reported in profiles that they would never actu-ally be recommended. A potential solution to this limitation, then, is to use InterestMap as a meta-level recommendation sys-tem, which simply passes on some context about a user (such as identity or style of music) onto a domain-specific feature-based recommender.

3.3 Identity and TasteWe have claimed that recommendations made with this corpus are indeed driven by identity and taste, but this requires some further explanation.

Earlier we have described that certain identity descriptors are in-ferred from the “special interests” category in profiles; so for example, “dogs” maps into “Dog Lover,” “reading” maps into “Book Lover”, and maybe “deconstruction” maps into “Intellec-tual.” In Section 4, we explain further how these inferential mappings are made. The space of identity descriptors is smaller and also less-sparse than the general space of interests. We cal-culate the following statistic in InterestMap: each identity de-scriptor occurs in the corpus on the average of 18 times more frequently than the typical interest descriptor. Recalling that the connection strengths in an InterestMap are learned by calculat -ing the pointwise mutual information (PMI) between any pair of features, the implication is that identity descriptor features are involved in many more calculations than the typical interest fea-ture, and are semantically connected to a broader set of features. A hub-and-spoke effect emerges from this, with each identity descriptor feature behaving as a hub, connected broadly and strongly through spokes to other features.

When InterestMap is used to make recommendations, spreading activation outward from a set of seed nodes determines the re-

sultant neighborhood from which recommendation candidates are selected. Since spreading activation tends to flow easily through strong links, and because a typical interest feature is, on average, more strongly connected to identity descriptor features than to other interest features, identity plays a dominant role in structuring recommendations.

Having established that identity is a major basis for recommen-dation, we have yet to argue that the connections between iden-tity descriptor features and interest features are warranted and meaningful. If an identity is thought of as a high-level theme describing a person, then that theme must lend some cohesion to the rest of the profile in order for it to be a useful organizer of interests. But why are we to believe that social network profiles demonstrate this kind of cohesion around high-level themes like identity?

Boyd’s (2004) observations that profiles are limited and focused in perspective and that users are sensitive to faux pas’s or out-of-place features offer some support to this end. Donath and Boyd’s (2004) suggestion that profile-crafting be viewed as an activity meant to signal underlying qualities about a person (like tastes or identity, for example) would require that many or most of the articulated interests coherently signal a single underlying quality because a single or a few interests might produce too weak or ambiguous a signal. In the sociology of identity litera-ture, there also a theoretical principle called the Diderot Effect (McCracken, 1991) which governs the assemblage of symbolic environments such as one’s possessions, or in our case, one’s self-descriptive profile. The Diderot Effect is “a force that en-courages the individual to maintain a cultural consistency in his/her complement of consumer goods,” (p. 123) and the ten-dency to be consistent with our articulated self-concepts; thus it is quite fair to conclude that one’s articulated identity-descrip-tors necessarily predict a coherency in one’s articulated inter-ests.

While many cultural identity descriptors are easy to articulate and can be expected to be given in the special interests category of the profile, tastes are often a fuzzy matter of aesthetics and may be harder to articulate in the special interests category. For example, a person in a European taste-echelon may like the band “Stereolab” and the philosopher “Jacques Derrida,” yet there may be no convenient keyword articulation to express this. This may be an example of the collective outsmarting the individual because once an InterestMap is learned, cliques of interests seemingly governed by nothing other than taste clearly emerge on the network. One clique for example, seems to demonstrate a latin aesthetic: “Manu Chao,” “Jorge Luis Borges,” “Tapas,” “Soccer,” “Bebel Gilberto,” “Samba Music.” In place of indexi-cals like identity descriptors, tastes seem to emerge as small cliques of interests which together constitute an indexical in the network. Because the cohesion of cliques is strong, they tend to behave as a single hub in the same way identity descriptor nodes behave in spreading activation.

4. IMPLEMENTATIONAs discussed above in Section 2, InterestMap is constructed in four steps. First, we mined 100,000 personal profiles from two web-based social networks between January and July of 2004. These profiles were organized as unstructured lists of interests under a fixed ontology of category headings such as “special in-terests,” “movies,” “books,” etc.

Second, we wrote Python scripts to segment these unstructured lists, which happened variously with commas, semicolons, new-lines, the word “and,” etc. To normalize the causally-stated key-words, we opted to create a formal ontology covering the space of interests. For this we turned to various resources of ontolo-gies on the web for music, sports, movies, television shows, and cuisines, including The Open Directory Project7, the Internet Movie Database8, and Wikipedia9. The ontology of cultural identity descriptors required the most intensive effort to assem-ble together, finished off with some hand editing. To assist in the heuristic normalization, we also gathered statistics on the popularity of certain features (most readily available in The Open Directory Project) which could be used for disambiguation (e.g. “Bach” “JS Bach” or “CPE Bach”?). Using this crafted ontology of 22,000 main features, the heuristic normal-ization process successfully recognized 68% of all tokens across the 100,000 personal profiles, committing 8% false positives across a random checked sample of 1,000 mapped features. We suggest that this is a good result considering the difficulties of working with free text input, and enormous space of potential features.

Third, once the interest features have been normalized, they are expanded using metadata assembled along with the formal on-tology. For example, a book implies its author, and a band im-plies its musical genre. These metadata features are included in the profile, but at a discount of 0.5 (read: they only count half as much). The purpose of doing this is to increase the chances that the learning algorithm will discover latent semantic connections.

Fourth, we apply an information-theoretic machine learning technique called pointwise mutual information (Church et al., 1991), or PMI, over the corpus of profiles to learn the semantic relatedness of every possible pair of features (interests or iden-tity-descriptors). For any two features f1 and f2, their PMI is given in equation (1).

(1)

What results is a 22,000 x 22,000 matrix of PMIs. After filtering out features which have a completely zeroed column of PMIs, we arrive at a 12,000 x 12,000 matrix, and this is the complete form of the InterestMap. Of course, this is too dense to be visu-alized as a semantic network, but we have built less dense se-mantic networks from the complete form of the InterestMap by applying thresholds for minimum connection strength.

5. EMPIRICAL EVALUATION We have thus far only performed an empirical evaluation of the performance of InterestMap versus a CF-like control in an inter-est recommendation task. We plan to further evaluate Inter-estMap through a real-world deployment so that we may mea-sure our claims regarding improved transparency and trust, and the impact that the identity-spokes and taste-cliques have on shaping recommendations. These will be included in a forth-coming paper.

We performed five-fold cross validation to determine the accu-racy of InterestMap in recommending interests, versus a control system which operates using naïve collaborative filtering. The corpus of 100,000 normalized and metadata-expanded profiles was randomly divided into five segments. One-by-one, each segment was held out as a test corpus and the other four used to either train an InterestMap using PMI, or stored as cases to be used for the CF control system.

Within each profile in the test corpus, a random half of the iden-tity-descriptor and interest features were used as a “situation feature set” and the remaining half as the “target feature set.” The InterestMap recommender uses the situation feature set to create an activation of the InterestMap network. Using this acti-vated network, a rank-ordered list of all interest features is cre -ated, and for each interest feature in the target feature set, a per -centile relevance score is calculated, corresponding to that target interest feature’s ranking in the list of all features. The overall accuracy is the arithmetic mean of the percentile relevance scores generated for each interest feature in the target feature set. We opted to score the accuracy of a recommendation on a sliding scale, rather than requiring that target interest features be guessed exactly within n tries because the size of the target fea-ture set is so incredibly small with respect to the space of possi-ble guesses that accuracies will be too low and variances too high for a good performance assessment.

Figure 2. Results of five-fold “hold-one-out” cross-validation of InterestMap and a CF control system on a recommendation task.

In the control system, the situation feature set is used to recall particular profiles containing those features, and profiles are scored and rank-ordered based on how many features from the situation feature set they contain. The interests contained in the rank-ordered list of profile matches are mapped out, duplicates filtered, and a rank-order list of interest features is produced. In both systems, scoring ties are broken by randomly ordering the tied items. The results are given in Figure 2.

The results demonstrate that on average, interest features in the target feature set were ranked found in the 82 percentile of fea-tures in InterestMap’s recommendations, and found in the 71 percentile in the CF control system; this is a rather unambiguous validation of the information theoretic “map” approach of rec-ommendation as compared to collaborative filtering. Of course, the patchy nature of this domain’s feature space must be consid-ered. Some regions in the feature space, such as music, seem to be densely covered, while other areas are quite sparse. Gener-ally, we would expect collaborative filtering to perform well in

7 http://www.dmoz.org8 http://www.imdb.com9 http://www.wikipedia.org

sparse domains, and information-theoretic approach to fare bet-ter in dense domains, but because this domain’s feature space is patchy, it is hard to assess the clear impact of the domain topol-ogy on the results. Standard deviation scores suggest that nei-ther system was entirely consistent in its performance, and are just straddling the line of acceptability. However, there are sev-eral odd-ball scenarios which may be artificially exaggerating these scores – for example, some interests in a profile may be completely out-of-the blue and apart from the others; also, er-rors in the normalization process may also artificially generate out-of-the-blue features.

6. CONCLUSION & FUTURE WORKApplying a social data mining approach to recommendation, we mined 100,000 self-descriptive social personal profiles from web-based social networks and applied a pointwise mutual in-formation learning mechanism to build InterestMap, a giant con-nectionist-semantic network of interests (i.e. hobbies, sports, music, books, television shows, movies, cuisines), and identity-descriptors (e.g. “raver,” “dog lover,” “fashionista”). We have argued that interest features derived from social network pro-files are non-arbitrary and have greater communicative value than features derived from histories of interactions with applica-tions, and thus, have more positive implications for transparency and trust; furthermore a “map” representation does a better job of protecting source data anonymity, and so is more appropriate when the source is sensitive to privacy like social network data. We also demonstrated the increased role that identity (via iden-tity-hubs) and tastes (via taste-cliques) play in the production of recommendations, and suggest that this has contributed to Inter-estMap’s 82% to 71% accuracy advantage over a collaborative filtering approach in our empirical evaluation.

In future work, we hope to explore the role that recognized iden-tities and tastes can play in generating explanations, and also how these interest grouping mechanisms can be used to learn from recommendation feedback (e.g. user did not like “ESPN Sportscenter” or “Blue Crush” so hypothesize that user is not “Sports Fan” and avoid all interests connected to this hub). We also see an opportunity to use InterestMap as a domain-generic recommender which can provide contextual support and boot-strapping recommendations to single-domain recommenders.

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