Boosting Trust in Collaborative Recommender Agents

with Interest Similarity

Daniela Godoy and Analıa Amandi

ISISTAN Research Institute, UNICEN University

Campus Universitario, Paraje Arroyo Seco

CP 7000, Tandil, Bs. As., Argentina

Also at CONICET, Consejo Nacional de Investigaciones Cientıficas y Tecnicas

{dgodoy,amandi}@exa.unicen.edu.ar

Abstract

Inserted in communities of people with similar interests, recommender agents predict thebehavior of users based on the behavior of other like-minded people. In addition to user sim-ilarity, trustworthiness is a factor that agents have to consider in the selection of reliablepartners for collaboration. Previous works focused on modeling trust in recommender sys-tems base on global user profile similarity or history of exchanged opinions. In this paper wepropose a novel approach for agent-based recommendation in which trust is independentlylearned and evolved for each pair of interest topics two users have in common. Experimen-tal results show that agents learning who to trust about certain topics reach better levels ofprecision than considering exclusively user similarity.

1 Introduction

Recommender Systems (RSs) have proven to be a valuable means in helping peopleto cope with the information overload problem. RSs based on collaborative filtering (CF)techniques rely on a collection of user profiles, usually given by relevance rating assignmentsfor a collection of items, to leverage the preferences of communities with similar interestsin order to generate high-quality recommendations.

In recent years, there has been an increasing interest in incorporating trust and reputationmodels into recommender systems in order to improve the accuracy and robustness ofrecommendations [15, 8, 11]. Trust-aware recommender systems base prediction on opinionsfrom reliable peers rather than from more similar ones. In these systems trust is establishedat a profile-level, i.e. considering the user entire behavior history or opinions. However,users might have diverse interests regarding different topics and on each of them beingconsidered differently trustworthy by other users. For example, a given person may behighly trustworthy referring to sport information but not when it comes to recommendabout political subjects.

In this paper we propose an agent-based recommendation paradigm in which a collectionof distributed content-based recommender agents learn multiple interest topics for differentusers and establish topic-level trust relationships with other agents for collaborative in-formation exchange. Under the assumption that trust must reflect user similarity to some

Simpósio Brasileiro de Sistemas Colaborativos

978-0-7695-3500-5/08 $25.00 © 2008 IEEE

DOI 10.1109/SBSC.2008.22

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extent, since recommendations only make sense when obtained from like-minded people [15],initial trust is boosted by similarity between shared interests. Further interaction allowsagents to establish a model of trustworthiness of other agents regarding distinctive topics,i.e. an agent learns from experience who to trust regarding individual interest topics.

The rest of the paper is organized as follows. Section 2 discusses related works ad-dressing the problem of modeling trust in recommender systems. Section 3 introduces theproposed recommendation approach in which agents not only focus collaboration on thoseinterests that are shared by users instead of using a global user similarity, but also acquiretrust in users regarding each interest in an independent fashion. Experimental results aresummarized in Section 4 and concluding remarks are stated in Section 5.

2 Related Works

Elicitation and propagation of trust about users have been explored in the context ofrecommender systems based on collaborative filtering (CF) techniques in several works [11,8, 15]. User-based CF algorithms focus recommendation on the opinions of like-mindedusers. In [8] trust statements explicitly expressed by users, i.e. users are asked how muchthey trust others for their ability to provide useful ratings, are organized in a trust networkand used to predict the trustworthiness of further users in recommendation. Likewise,FilmTrust1 [4] recommender system explores trust in Web-based social networks. In thissystem, users can rate films, write reviews and also express trust in other users regardingmovie ratings. The study reported in [15] investigates the interaction between trust andinterest similarity using this system as one of the data sources. The authors concluded thatdependencies between trust and interest similarity exist when the trust network is tightlybounded to some particular application (e.g. movie or book recommendation).

The need for explicit trustworthiness statements suffers from some limitations as it im-plies an additional user effort whose benefit is not observed immediately, particularly inthe case of new users that have to build up their trust networks before using the systemfor obtaining recommendations. Other works derive trust from rating-based data (i.e. amatrix in which rows represent users, columns represent items and entries are user rat-ings for items). In [11] trust relationships are inferred using the rating matrix as inputdata. Trust is computed at both profile-level, average trust for the profile overall, anditem-level, average trust for a particular profile when it comes to making recommendationsfor a specific item. The idea of topic-level trust, in which classification taxonomies as theones available in several e-commerce systems are used to categorize items, is discussed andevaluated in [14]. Both item and topic-level trust incorporated into classical user-based CFalgorithms outperform profile-level trust in the reported results.

In contrast to these works, which focused on computational trust models as a means toimprove or replace standard rating-based CF approaches, we propose to introduce topic-level trust in a context of distributed content-based agents, each learning information pref-erences of individual users for personalization. More directly related to this work is theopinion-based filtering method proposed in [10], which derives trust from user similarity.In this case, trust values are computed by pairs of agents on the basis of a conversationalexchange in which one agent solicits the opinions of others with respect to a set of items.Rather than user similarity calculated based on a set of opinions, a more focused interest

1http://trust.mindswap.org/FilmTrust/

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similarity is proposed in this work to boost trust in other agents regarding different interesttopics.

3 Agent-based Recommendation Approach

The idea of personal agents acting on behalf of users and sharing knowledge has beengenerally hampered by the assumption that agents begin with a predefined, common ontol-ogy instead of personalized and diverse ontologies [13]. However, a single general-purposeontology can hardly embrace the diverse and highly specific interests users might have invast information spaces such as the Web. The approach presented in this paper divergesfrom the common ontology paradigm to a paradigm involving agents that acquire topicaltaxonomies describing user preferences as a starting point for creating communities withshared interests.

In this setting, a recommender system is composed of a set of personal agents, A ={a1, . . . , an}, helping users to find relevant information according to their current needs.Each agent is responsible for acquiring an accurate profile representing a user informationpreferences and delivering both content-based and collaborative recommendations. Userprofiles model the topics users are interested in as taxonomies of hierarchically organizedconcepts, denoted C = {c1, c2, . . . , cn}, each being the internal representation of a topic suchas sports, finances, etc. Section 3.1 briefly describes the approach used to learn profiles.Section 3.2 summarizes the method used for profile comparison. Section 3.3 presents thetrust-aware recommendation approach.

3.1 Learning Interest Taxonomies

User profiles are considered to be topic taxonomies that agents learn through observationof user behavior on the Web. The hierarchical structure of user interests is exploited tocompute meaningful similarities between topics existing in two profiles instead of using aglobal profile similarity. User profiling approaches which concentrate on obtaining semanti-cally enriched profiles through the representation of hierarchical organized concepts a useris interested in, such as ontology-based user profiling [9, 7] or profiling based on cluster-ing algorithms [6], can be used for obtaining interest taxonomies. In this work, we haveused a conceptual clustering algorithm with structures and procedures specifically designedfor user profiling, which carries out incremental unsupervised concept learning over Webdocuments [3].

Experiences representing user interests collected by agents through observation (e.g. aWeb page the user reads) are analyzed to learn a conceptual description of such interests.Thus, user profiles are built starting from scratch and are constantly refined as new ex-amples of user interests become available, using relevance feedback as the main source ofinformation. Each experience ei collected by a personal agent is attached with a relevancevalue denoted reli, i.e. profiles consist of pairs 〈ei, reli〉, where ei is the experience encod-ing mainly the Web page the user found interesting, and reli represents the evidence aboutthe user interest in that experience. Initially, relevance values are determined by evidencecollected from observation (e.g., using implicit interest indicators such as the time spenton a page, the amount of scrolling, etc.). Then, relevance values are used for both gainingconfidence in experiences which are more representative of the user interests, those leading

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to successful recommendations, as well as forgetting old experiences belonging to no longerinteresting topics.

In order to automatically assign experiences to topics, a text-like description given bya set of weighted terms, ci = 〈(l1, w1), ..., (lm, wm)〉, is associated to them during learning.This description constitutes a classifier for the topic and emerges from observing the com-mon features of experiences in the topic and those a novel experience should have in orderto belong to it. Leaves in the hierarchy correspond to clusters of experiences belonging toall the ancestor topics. Intuitively, clusters are groups of experiences whose members aremore similar to one another than to members of other clusters. Thus, a set of ni experi-ences belonging to a topic or concept ci, denoted Ei = {e1, e2, . . . , eni

}, is organized into acollection of k clusters below it. Given a sequential presentation of experiences and theirassociated descriptions, an incremental conceptual clustering algorithm [3] is used for find-ing clusters that group experiences into concepts, obtaining a description of each concept(i.e. a linear classifier) and a hierarchical organization of them.

3.2 Computing Interest Similarity

Loosely coupled groups of agents assisting users with similar interests can take advantageof the knowledge available in the community they are immersed in through cooperation.It has been argued that given some predefined domain and context, people create ties offriendship and trust primarily with people resembling their own interest profile [1]. Thus,trust in other users is boosted using interest similarity and later refined based on interactionexperiences with other agents.

To automatize the process of evaluating the similarity among interest topics across tax-onomies learned by personal agents, we applied the Triple Matching-Model (MD3) proposedby [12]. MD3 aims at finding quantitative values of similarity among concepts in two sep-arated ontologies by comparing concept descriptors and interrelationships. The resultingsimilarities allow agents to establish links between two taxonomies while keeping each ofthem autonomous. This is important in dynamic user profiling since profiles are expectedto change over time and links need to be adapted accordingly.

The MD3 matching process is defined over a single hierarchy resulting from connect-ing two independent concept hierarchies via an imaginary root. Using the interconnectedhierarchies, the matching process is applied in successive steps to different components:(1) lexicon matching, (2) feature matching, and (3) semantic-neighborhood matching. Thesimilarity value S(a, b) between two concepts a and b belonging to two different profiles, p

and q, is the weighted sum of the similarity of each of these components:

S(ap, bq) = wl ∗ Sl(ap, bq) + wf ∗ Sf (ap, bq) + wn ∗ Sn(ap, bq) (1)

where Sl, Sf and Sn denote the lexical (i.e., the similarity among names), feature andsemantic neighborhood similarities, respectively, and wl, wf and wn are their weights.Lexical similarity is not considered in this work since topics have no associated labels.

The feature matching Sf (ap, bq) between two concepts ap and bq, where A and B are therespective term sets, is determined by the cardinality of the intersection and the differencebetween A and B as follows:

Sf (ap, bq) =|A ∩ B|

|A ∩ B| + α(ap, bq) ∗ |A − B| + (1 − α(ap, bq)) ∗ |B − A|(2)

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for 0 ≤ α ≤ 1. The value of α is a function of the depth of concepts, i.e. the shortestdistance from the concepts to the virtual root:

α(ap, bq) =

{

depth(ap)depth(ap)+depth(bq) if depth(ap) ≤ depth(bq)

1 − depth(ap)depth(ap)+depth(bq) if depth(ap) > depth(bq)

(3)

MD3 also includes hierarchical relationships in the matching process assuming that twoconcepts are semantically similar if they are related to the same group of concepts. Thenotion of semantic neighborhood, the set of concepts whose distance to a given concept iswithin a specified radius, is used to calculate neighborhood similarity. The distance betweentwo concepts is measured as the shortest path, i.e. the smallest number of undirectedarcs that connect these concepts. Equation 4 gives a formal definition of the semanticneighborhood N , where ao and co

i are concepts in an ontology o, r is the specified radius,and d(ao, co

i ) is the distance between the two concepts.

N(ao, r) = {coi } such that ∀i d(ao, co

i ) ≤ r (4)

Given two concepts ap and bq from ontologies p and q, where N(ap, r) has n conceptsand N(bq, r) has m concepts, and the intersection of the two neighborhoods is denotedby ap ∩ bq, the semantic-neighborhood matching is a function of the cardinality of thesemantic neighborhoods N and the approximate intersection ∩n between these semantic-neighborhoods, calculated by:

Sn(ap, bq, r) =|ap ∩n bq|

|ap ∩n bq| + α(ap, bq) ∗ δ(ap, ap ∩n bq, r) + (1 − α(ap, bq)) ∗ δ (bq, ap ∩n bq, r)(5)

with

δ (ap, ap ∩n bq, r) =

{

|N(ap, r)| − |ap ∩n bq| if |N(ap, r)| > |ap ∩n bq|

0 otherwise(6)

We considered a radious of 1 in further experiments in order to compare the number ofshared concepts in the immediate neighborhood. The intersection over semantic neighbor-hoods is approximated by the similarity of concepts across neighborhoods:

|ap ∩n bq| =

∑

i≤n

(

maxj≤m

S(api , b

pj )

)

− ϕ ∗ S(ap, bq) (7)

with

ϕ =

{

1 if S(ap, bq) = maxj≤m S(ap, bqj)

0 otherwise(8)

where S is the semantic similarity of concepts between two concepts api and b

qj from the

neighborhoods of ap and bq and n and m are the numbers of concepts in the correspondingsemantic neighborhoods. This semantic similarity is calculated using lexical and featuresimilarity as follows:

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S(api , b

qj) = w′

l ∗ Sl(api , b

qj) + w′

f ∗ Sf (api , b

qj)) (9)

For each pair of users, a profile comparison algorithm [2] based on MD3 is applied tocompare topics across interest taxonomies.

3.3 Recommendation Methods

3.3.1 Interest-Based Recommendation

To recommend a previously unseen Web page, an agent retrieves similar experiencesfrom the user profile assuming that the user interest in the new page will resemble theinterest in similar past experiences. Hierarchical topics in the profile bias the search towardthe most relevant experiences. Thus, the confidence agent ai has in a given candidaterecommendation r, denoted confai

(r), is calculated by a function that aggregates the globalsimilarity of each retrieved experience ej with the page r and the experience relevance. Thatis, to assess the confidence in recommending r given the experience ej , a weighted sum ofthe confidence value of each similar retrieved experience is calculated as follows:

confai(r) =

∑nj=1 wj ∗ relj∑n

j=1 wj

(10)

where n is the number of similar experiences exceeding a predefined similarity thresholdthat are retrieved, relj is the relevance of experience ej and wj is the contribution of eachexperience according to its similarity based on the well-known distance-weighted nearestneighbor algorithm. Each experience has a weight wj according to the inverse square of itsdistance from r, i.e. wj = 1

(1−sim(ej ,r))2, where sim(ej , r) is the similarity between the item

to be recommended r and the retrieved experience ej . If the confidence in recommendingr is greater than a certain threshold, the page is recommended.

3.3.2 Collaborative-Based Recommendation

To enable information exchange among users, agents have to attain knowledge aboutother users and find the most appropriate ones for collaboration. In the proposed recom-mender system each personal agent is registered to one or more matchmaker agents thataccomplish this goal, i.e., matchmakers are in charge of making connections between agentsthat request recommendations or opinions and those that are able to provide them. Forthis purpose, agents provide matchmakers with knowledge about the main interests of theirusers (i.e., topical taxonomy, but not experiences), resources discovered about shared in-terests (i.e., candidate recommendations) and, optionally, user current information needs(e.g., a query or categories the user is looking information about). In this situation, thematchmaker agent determines which agents are promising candidates for a fruitful cooper-ation.

Interest similarities are discovered by matchmakers applying the profile comparisonmethod to each pair of users. If the comparison of profiles between two users assistedby agents ai and aj is successful, agents become aware of the concepts their users sharewith other members of the community. For example, if both users are interested in topics cp

and cq, the associated agents store social knowledge registering the existence of other agentswhich might be interested in information regarding these concepts. This social knowledge

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allows agents to notify matchmakers about new relevant information when it is discoveredby their users.

Matchmakers, on the other hand, store knowledge gathered from the group of personalagents they are managing. In the previous example, after comparing the profiles of bothusers the matchmaker agent stores knowledge stating that both ai and aj are interested inconcepts cp and cq with a certain degree of confidence given by the corresponding similarityvalues, i.e. simcp

(ai, aj) = S(caip , c

ajp ) and simcq

(ai, aj) = S(caiq , c

ajq ). Hence, each time

agent aj finds novel information about cp or cq, agent ai is contacted to receive it. Theconfidence of agent ai in a candidate recommendation r belonging to a concept co is:

confai(rco) = simco

(ai, aj) ∗ confaj(rco) (11)

Ultimately, a recommendation is deemed relevant for a given user if the page exceeds cer-tain confidence threshold, which can be customized according to the amount of informationeach user is willing to receive from others.

3.3.3 Trust-Aware Recommendation

In the trust-aware version of the collaborative approach, recommendations are obtainedfrom reliable agents, reducing the group of candidate users to those that can be trustedregarding a given interest topic a user share with other members of the community. Thetrust value agent ai has in another agent aj regarding a certain interest co, denoted tco

ij , isrepresented with a real value between 0 and 1, i.e. tco

ij ∈ [0, 1], where 1 represents that ai

blindly relies on aj concerning co and 0 that ai totally mistrusts aj in this topic of interestboth users share.

Initially, trust values assigned to agents in different topics are boosted using interestsimilarities, i.e. tco

ij = simco(ai, aj). The confidence in recommending an item rco belonging

to an interest co based on the opinion of an agent aj and the trust conferred to this agent isgiven by tco

ij ∗ confaj(rco). If the opinions of several agents are taken into consideration, the

confidence value in a candidate recommendation is calculated by aggregating the differentagent opinions and trust values as follows:

confai(rco) =

∑

ajtco

ij ∗ confaj(rco)

∑

aj

(12)

Once trust relationships are established based on interest similarity, agents start learningfrom their experience in dealing with peers and update the trust in them accordingly.Further trust values assigned to agents are directly connected with the outcome of therecommendation process. The real interest of a user in a Web page, denoted intreal, canbe inferred by agents based on either implicit or explicit feedback mechanisms. Hence,the opinion of each agent is compared with the real interest showed by the user in therecommended item and trust values are adapted as follows:

tco

ij = ϕ ∗ tco

ij + (1 − ϕ) ∗(

1 −∣

∣intreal − confaj(rco)

∣

∣

)

(13)

where ϕ is the trust modifying factor that manages the dynamics of trust. This functionwas initially proposed by [5] and also applied in [10]. If ϕ = 0.8 trust is updated slowly forpositive opinions and fast otherwise, whereas ϕ = 0.5 leads to slow adaptation to negativeopinions and fast to positive ones.

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User 1 User 2 User 3 User 4 User 5 User 6

Bands cold hot hot - - -

BioMedical hot - - hot cold -

Goats - cold - hot hot

Sheep - - cold - hot hot

Table 1. Relevance judgments given by the target user to pages used for training

the other users

4 Experimental Results

We used Syskill&Webert Web Page Ratings collection2, which contains the ratings of asingle user about the interestingness of Web pages, for carrying out an empirical evaluationof the proposed approach. In this collection Web pages belong to four different categories:Bands (61), Goats (136), Sheep (70) and BioMedical (65). In addition to the topicalclassification, a single user manually rated each page in a three point scale: hot or veryinteresting (93), medium or quite interesting (11) and cold or not interesting at all (223).From the original collection we removed ‘Not Found’ pages and those pages with notassigned rating, yielding to a total of 327 pages.

For the user who assigned rating to Web pages in the collection, regarded as the targetuser hereafter, the impact of trust on the received recommendations was evaluated. Tocreate the profile of this user we randomly selected 233 pages from the collection (≃ 70%)that were used for training, reserving the remaining 94 pages (≃ 30%) for testing. Thispartition was made trying to keep the same proportion of examples in each class (hot,medium and cold), hot and medium pages in the training set were added to the profile ofthe target user with an initial relevance of 1 and 0.5 respectively.

In order to evaluate the role of trust and interest similarity in collaborative recom-mendation, we created a group of users each interested in two of the categories in theSyskill&Webert collection. For each of these users, we built a profile using a training set ofselected experiences belonging to each category (≃ 50%). Using this method, we created atotal of six users interested in differently combined pairs of topics, leading to taxonomieshaving at least two concepts and, possibly, some sub-concepts.

For simulating a scenario in which users have some interest similarities, but are notnecessarily reliable on these shared interests, in some topics the experiences were selectedfrom cold pages and in others from hot and medium pages. Table 1 shows the relevancejudgments given by the target user to pages used to learn the profiles of the other six users.Initial relevance of experiences in the profiles of users was assigned randomly. Thus, eventhough the training sets used for the target and other users are overlapping, the relevanceassigned to shared experiences is different in each case, causing the profiles to be focused ondifferent aspects. Moreover, the taxonomies of the target and other users will exhibit certaintopical similarities, but these users not necessarily will provide good recommendations asthey were trained using uninteresting pages.

Figure 1(a) shows the similarities between the target and other users regarding thedifferent topics in the respective hierarchies as well as the average profile similarity. Labelswere added to the figure for illustrative purpose only, but concepts in the hierarchies aredescribed by a set of terms (e.g., 〈goat,milk〉). It is possible to observe in the figure that

2http://kdd.ics.uci.edu/databases/SyskillWebert/Syskill Webert.html

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0

0.2

0.4

0.6

0.8

1

User 1 User 2 User 3 User 4 User 5 User 6

Inte

rest

Sim

ilarity

BandsBioMedical

SheepGoats

Average

(a)

0.5

0.52

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7

1 8 16 24 32 40 48 56 64 72 80 88 94

Tru

st

# Testing Experience

User 1 − BandsUser 1 − BioMedical

User 2 − GoatsUser 2 − Bands

(b)

Figure 1. Initial interest similarities and evolution of trust values from the target user

perspective

the method for comparing hierarchies effectively distinguished the concepts users had incommon. Conversely, if the average profile similarities had been considered users wouldhave received information about some uninteresting, non-shared topics.

Figures 2(a) and (b) show the evolution of precision (i.e. proportion of relevant items outof the total number of recommendations) and accuracy (i.e. proportion of correct decisionsover the total) in collaborative recommendations as testing pages were presented, followinga random order and using a confidence threshold equal to 0.6. When only interest similaritywas considered precision of recommendations was poor, since the agent assisting the targetuser was not capable of identifying that some users were not providing good recommenda-tions in spite of the interest similarities. Learning trust values during interaction, however,allowed this agent to recognize untrustworthy agents in some topics.

In spite of being less accurate than content-based recommendations, collaborative onesusually entail higher novelty (non-obviousness) and also in terms of accuracy trust-awarerecommendations outperform collaborative ones based on interest similarity alone. Fig-ure 1(b) depicts some examples showing the evolution of trust regarding certain topicsfrom the agent assisting the target user perspective. For example, the agent loses the trustgranted to user 1 regarding Bands, but maintains certain trust in user 2 about this topic.Likewise, trust in user 2 regarding Goats diminished over time, whereas trust in user 1about BioMedical tends to increase.

5 Conclusions

In this work, we proposed an agent-based recommendation approach that takes advantageof detailed representations of user interests learned by personal agents and provides amechanism for deciding whether to trust other agents regarding interest topics shared bytheir users. The advantage of this approach is twofold. First, partially similar interests areconsidered so that users can agree with others only on certain aspects of their preferencesinstead of using global user similarities. Second, trust is boosted by these partial interestsimilarities and evolves according to the interaction experiences with other agents regardingeach interest independently.

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0

0.2

0.4

0.6

0.8

1

1 8 16 24 32 40 48 56 64 72 80 88 94

Pre

cis

ion

# Testing Experiences

Interest SimilarityTrustworthiness

(a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 8 16 24 32 40 48 56 64 72 80 88 94

Accura

cy

# Testing Experiences

Interest SimilarityTrustworthiness

(b)

Figure 2. Performance of cooperative agents in recommendation

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