Trust-based Recommender Systems: An overview

Trust-based Recommender Systems: An overview Afef Selmi, PhD.Student, Tunis, Tunisia, [email protected] Zaki Brahmi, Assistant Professor, Sousse, Tunisia, [email protected] Mohamed Mohsen Gammoudi, Full Professor, Tunis, Tunisia, [email protected]

Abstract In a highly dynamic and decentralized environment, where data are uncertain, Trust has become a key factor in the process of decision making. Trust-based recommendation is based on Trust between users. It was the main subject of several studies such as: Haydar (2014), Simon et al (2012), Fabiana et al (2011), Golbeck (2005), Josang and Pope (2005). In fact, for relevant recommendation, it is very important to define the adequate techniques for modeling and evaluating trust between agents. In this paper, we give a state of the art of modeling trust in recommender systems. Furthermore, we make a comparative study between various existing methods. Keywords: trust, trust relationship, trust models, trust-based recommender system.

1. INTRODUCTION Simon et al (2012) mentioned that internet has become a tool used in several activities such as personal activities, educational activities, associative activities, etc. The available resource information which is heterogeneous and huge make the user perplexed to find the relevant information. To address this problem, classical information retrieval systems need an intelligent component to help user during his retrieval session. This component is defined as a recommender system. The main objective of this component is to provide information based on user needs and preferences. The task of recommendation is decomposed on subtasks which are realized by appropriate agents. To ensure a good recommendation, agents must cooperate between them. The cooperation assumes that there is a trust relationship between agents. The trust is defined in some works by some measures and modeled as a trust network. In this paper, we present a state of the art on different methods of trust based recommender systems. The rest of the paper is organized as follows: the second section focuses on the recall of basic concepts of trust. The third section contains a state of the art of existing trust models used in recommender systems. The fourth section, describes a comparative study between these different models while emphasizing their advantages and disadvantages. At the end, we give a conclusion and some future works.

2. BASIC CONCEPTS OF TRUST Trust has become a functional necessity in a social system that is characterized by the development of interpersonal relationships. In fact, the presence of this type of relationship encourages the sharing of knowledge. In Connelly and Kelloway (2000), trust is the most important condition for sharing knowledge between users. In fact, Trust is a general concept that can be applied to any context. It plays an important role in several disciplines such as: sociology, psychology, computer sciences, recommender systems, etc.

In the following section, we present trust definitions and we describe their properties. At the end, we explain how trust value could be used as a binary or fuzzy value.

2.1 Definitions of trust Trust has been defined in several fields such as psychology Deutsch (1962), sociology Dumouchel (2002), computer sciences Golbeck (2005) and recommender system Victor et al (2011). In Psychology, Deutsch (1962) defined trust as: “the individual is confronted with an ambiguous path, a path that can lead to an event perceived to be beneficial (Va+) or to an event perceived to be harmful (Va)”. He perceives that the occurrence of Va+ or Va- is contingent on the behavior of another person and he perceives the strength of Va- to be greater than the strength of Va+. In Sociology, Dumouchel (2002) described trust as: “a bet about the future contingent actions of the trustee. This bet, or expectation, is considered to be trust only if it has some consequence upon the action of the person who makes the bet (i.e., trustor)”. In computer sciences, Golbeck (2005) defined trust as: “a commitment to believe in the smooth running of the future actions of another entity”. In another manner, Entity A trusts entity B, means the satisfaction of A on the performance of a task realized by B. In recommender system, Victor et al (2011) described trust as: “the local belief of one user in the usefulness of recommendation provided by another user”.

2.2 Properties of trust In the web based social environment, trust has been described by some properties Bhuiyan et al (2010) and Golbeck (2005). These properties identify where trust exists in social networks, and how it can be used in computation Sana (2016). In the following, we present some properties that are proposed by Golbeck (2005): -

Trust is asymmetric: if a user A trusts user B, this doesn’t mean that B trusts A. Trust is not distributive: if user A trusts user B and C, this doesn’t mean that A trusts B and A trusts C. Trust is not generic: if user A trusts user B in computer science, this doesn’t mean that A trusts B in the health field. The property of transitivity is a challenge in trust modeling. Little works consider that trust is not transitive Abdul-Rahman and Hailes (2000), which goes against the majority of works that consider the transitivity as a critical feature in the modeling phase Haydar (2014) Herzig et al (2010), Josang and Pope (2005) and Simon et al (2012). There are also several works assume that trust is transitive but this transitivity needs certain constraints Christiansoon and Harbison (1996) and Josang and Pope (2005).

2.3 Values of trust In social networking, trust present information about a social relationship between two users. This relationship is represented by a label. The label is described with different manners on different social networking such as Epinionsi, Advogatoii. The Epinions web of trust is a who-trust-whom online social network of a general consumer review site Epinions.com. Members of the site can decide whether to trust each other. The network consist of individual uses connected by directed trust and distrust links. Edges have the weight +1 for trust and -1 for distrust. Advogato is an online community and social networking site for developers of free software. Users of this site can publish news about software they have released or post updated on anything they are working on.

In fact, users certify each other in a kind of peer review process. On Advogato, nodes are users and the directed edges represent trust relationships. A trust link is called “certification”. Three different levels of certifications are possible on Advogato, corresponding to three different edge weights: Apprentice (0.6), Journer (0.8) and Master (1.0).

3. MODELING TRUST IN RECOMMENDER SYSTEM: STATE OF THE ART According to Ma et al (2009), Massa and Avesani (2007) and O’Donovan and Smyth (2005), trust-based recommender systems are collaborative systems based on user relations who express trust between them. A famous example is the Epinions website, which recommend items liked by trusted users. In fact, trust between two user means that a user believes on the utility of the recommendation of a trusted user. Several Trust-based systems have been proposed since more than two decades. They affect many areas such as: semantic web Donovan and Yolanda (2007) and Wolfgang et al (2004), multi-agents systems Herzig et al (2010), cloud computing Wenjuan and Lingdi (2009), recommender system Massa and Bhattacharjee (2004) and multi-agent recommendation systems Fabiana et al (2010), etc. In our work, we are interested on trusted relationships between users taking into account their skills in a given context (we mean by context the field of user’s expertise). The relationship between users depends on the skill areas. In the following sections, we examine different modes for trust based recommender systems. To do this, we classified these models into two categories: those that take the skill domain trust and those who doesn’t take this property.

3.1 Approach with trust contextualization Model of Abdul-Rahman In Abdul-Rahman and Hailes (2000), the authors proposed a trust model that takes into account the trust context. They define trust as a subjective measure or a belief on a personal experience in a given context. This Belief takes a value among four values: very bad, bad, good and very good according to the user’s opinion. For Abdul-Rahman, trust between two users is determined only by the interactions between them, since transitivity isn’t taken into account. Two types of interactions are possible: the evaluation of experiences between two users and the acceptance of recommendations from user. In this model, each user u has two sets X and Y where X contains his interactions and Y stores his opinions about users who provide recommendations. Each element of X or Y is composed by a triplet (us, c, S); witch us, c and S represent respectively the affected user (the user who’s trusted by u in the case of X or the user who recommended an item to u in the case of Y), the context (the topic of the task) and the set of user’s opinions. An opinion can have four values: very bad, bad, good and very good. Each element s of S represent an opinion of u about a user v in a context c. The opinion s is defined by a vector of four counters corresponding to the four values of the opinion. When u expresses an opinion about an interaction with v in a given context c, the counter corresponding to the value is updated. This vector is represented as follows: 𝑺𝒖 = {(𝒔𝒗𝒃 , 𝒔𝒃 , 𝒔𝒈 , 𝒔𝒗𝒈 ) , (𝒔𝒗𝒃 , 𝒔𝒃 , 𝒔𝒈 , 𝒔𝒗𝒈 ) } 𝒗𝒄𝟎

𝒗𝒄𝟏

Where 𝑣𝑏, 𝑏, 𝑔, 𝑣𝑔 denote respectively “very bad”, “bad”, “good” and “very good”. The degree of the trust between u and v is the max between values of the four counters. While this model is among the first that took into account the context of trust, it didn’t take the transitivity of trust. Seen that the opinions of credible friends provide a positive effect on system performance. In Josang and Pope (2005), authors confirm that transitivity is very important for improving performance of trust-based recommender systems.

Model of Charif Alchiekh Haydar The author proposes a three trust models taking into account the trust context such as: a local trust model, a collective trust model and a global trust model. The models are based on subjective logic Haydar (2014). For each model, the relationships between users are modeled by a trusted network. The trust relationship between two users X and Y is given by the opinion (according to subjective logic) of X on Y. In the first model, each user uses his own opinions, and consults those of his friends in the absence of his own ones. The purpose of this model is to predict the response that the user will accept for his question. Formally, the final score is given by the following formula: 𝒆(𝒂, 𝒓) 𝒔𝒄𝒐𝒓𝒆(𝒓) =

∑⊕ [𝒆(𝒂, 𝒇𝒋 ) ⊗ 𝒆(𝒇𝒋 , 𝒓)] {

𝒊𝒇 𝒆(𝒂, 𝒓) ∈ 𝑬 𝒆𝒍𝒔𝒆

𝒋

Where 𝐸 and 𝑒(𝑎, 𝑟) represent respectively a set of opinions and a personal opinion between two users 𝑎 and 𝑟. Compared to the previous model, in the collective trust model, direct interactions between users aren’t always sufficient to provide relevant information on the user. For this reason, collective opinions are used in all cases in this model. Thus, the user requests always the opinion of his friends to consolidate his opinion. The function that determines the opinions is given by the following formula: 𝒆(𝒂, 𝒓) ⊕ ∑⊕ [𝒆(𝒂, 𝒇𝒋 ) ⊗ 𝒆(𝒇𝒋 , 𝒓)]

𝒊𝒇 𝒆(𝒂, 𝒓) ∈ 𝑬

𝒋

𝒔𝒄𝒐𝒓𝒆(𝒓) =

∑⊕ [𝒆(𝒂, 𝒇𝒋 ) ⊗ 𝒆(𝒇𝒋 , 𝒓)] {

𝒆𝒍𝒔𝒆

𝒋

For the context of trust, a global trust model is proposed by Haydar (2014), that is to say, the user X relies on the reputation of the target user to decide whether to cooperate with him or not. In this context model, the reputation score of a user is not absolute; it varies depending on the keywords extracted from the questions that the target gave a correct answer. Thus, the user profile is created based on its reputation by keywords. When the user provides an accepted response to a question, a link (opinion) is established between him and each of keywords associated with the question. After determining the reputation scores for different users who responded to a question, the new model attempts to order the list of users with the aim of predicting the accepted answer. The user with the highest reputation score will provide the accepted answer.

3.2 Approach without trust contextualization MoleTrust MoleTrust is a trust model proposed by Massa and Bhattacharjee (2004). In this model, Trust is defined by a binary value. The trust propagation is a basic property in MoleTrust. It is assumed that trust degree between X and Z isn’t the same trust degree between X and Y. The propagation of Trust is expressed by transitivity. In fact, when user X trusts user Y, and Y trusts user Z, then X trust Z. In order to fix the trust propagation, authors defined a propagation having 4 as maximum distance between two users. MoleTrust predicts the trust value between to users X and Z by using the following formula:

(d − n + 1) if n ≤ d d 0 if n > 𝑑 Where d is the maximal distance of propagation and n is the distance between X and Z. Using transitivity, n = 2 because, there is only one intermediate between X and Z. tr(X, Z) = {

When existing several paths between X and Z, MoleTrust takes the shortest path and considers it the best one which maximizes the trust value. Compared to collaborative filtering, MoleTrust showed his performance in terms of recommendation accuracy. However, the choice of the shortest path doesn’t always guarantee the best performance. Since, we can find a longer path with more knowledge about the target. TidalTrust In Golbeck (2005), author proposed the model TidalTrust for social networks. It is dedicated to recommend movies to users. In this model, each user can evaluate movies with a scale of 1 to 5 stars. He can also evaluate his trust to another user with a scale of discrete values in [1, 10]. In fact, the trust networking between users is represented by a directed graph. TidalTrust allows a source to deduct the score of a movie m from the recommendation scores of other evaluators of the same movie. Formally, the recommendation score rsm deduced by a source s of a movie m is determined by the following formula: ∑𝐬 ∈ 𝐚𝐝𝐣(𝐒) 𝐭 𝐬𝐢 × 𝐫𝐢𝐦 𝐫𝐬𝐦 = ∑𝐬 ∈ 𝐚𝐝𝐣(𝐒) 𝐭 𝐬𝐢 Where: adj(s) ∈ S, t si and rim represent respectively the nodes directly connected to the source, the trust score between the node s and its neighbor and the score of the movie m given by the node i. Compared to MoleTrust, Massa and Bhattacharjee (2004), in which the trust value is binary, TidalTrust allows the user to express a gradual trust to other users. However, this model didn’t take into account the trust contextualization. Also, it didn’t consider neither the length of path nor the various possible paths. In addition, author hadn’t studied the risk, that’s to say he hadn’t defined a threshold that allows the user to choose the movie or not. Model of O’Donovan Authors propose a model for trust-based recommendation systems. It is mainly based on collaborative filtering. The main idea of this model is to add a layer of trust to the collaborative filtering with changes in the used terms. In this new model, the user is called “consumer” and the neighbors are called “producers”. To add trust to the collaborative filtering, three methods are proposed: a weighting method, a filtering method and a combining method, O’Donovan and Smyth (2005). The first method consists on replacing the similarity in the collaborative filtering by the value w (c, p, i) where c represents the consumer, p represents the producer and i represents the item. Formally, it is defined by the following formula: 2 × 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑐, 𝑝) × 𝑟𝑒𝑝𝑢𝑡𝑎𝑡𝑖𝑜𝑛(𝑝, 𝑖) 𝑤(𝑐, 𝑝, 𝑖) = 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑐, 𝑝) + 𝑟𝑒𝑝𝑢𝑡𝑎𝑡𝑖𝑜𝑛(𝑝, 𝑖) Knowing the reputation of a user which is expressed by the percentage of correct predictions in which he was produced. The second method consists of three steps: Firstly, the determination of the set of neighbors using the collaborative filtering. Secondly, selection of only neighbors with a reputation of the item which exceeds a threshold using a trust filters. Thirdly, the prediction of the recommended items by applying the same formula used for collaborative filtering is done.

The third method is called hybrid because it combines the two previous ones. In fact, after determining the set of neighbors by collaborative filtering (used in second method), the hybrid method computes the weighting using the formula w(c, p, i) defined in the first method. Although this model shows its effectiveness in improving the performance of collaborative filtering, Haydar (2014). However, it didn’t take into account the trust contextualization. Model of Simon The author proposes a social recommender system based on the social connections between the users. In this system, only trusted users can communicate. Thus, trust is an explicit value. The purpose of this system is to predict the missing notes which are called scores between a user and an object. To do this, they proposed an algorithm based on five different steps which allow obtaining the missing scores. Each step tries to perform the last one and they are more described in Simon et al (2012). The first step called Immediate Social Scoring, determines the score of an object i by user a. In fact, it is equal to the score between them; if it exists elsewhere the score is the combination of the different scores of his friends. The second step called K-Depth Social Scoring, is an extension of the first one. Its principle is to use the friends’ propagation which means to apply the first step on friends of friends to a deep K. The third step called Correlative Social Scoring, the authors introduced a correlation coefficient between users to refine trust between them. The correlation is calculated only among direct friends. Relative Social Scoring is the fourth step which computes a relative score that is defined by the difference for each user between the score and the average of ratings. Furthermore, it aggregates and adds the relative scores for the average of scores of the affected user. The last step of the algorithm called CorrRelative Social Scoring which consists of a combination of different steps. Although this model shows its effectiveness in terms of prediction, experimental studies have shown that it has given poor coverage using direct relations (k=1) compared to other algorithms of recommendation. Propagating at a depth k=2, to improve coverage, the obtained results show that this propagation implied a loss of accuracy.

4. COMPARATIVE STUDY OF TRUST MODELS In this section, we present a comparative study on different trust models. We take the comparison criteria used in Haydar (2014). Furthermore, we observe that there are two kinds of techniques for trust modelling. The first one is based on mathematical foundation such as subjective logic and the second one is based on measure to predict the trust scores. To differentiate between models according to this observation, we use the criteria “the used technique”. Criteria’s used in Haydar (2014) are: trust relationship, trust note, trust value, trust propagation, trust aggregation and trust contextualization. Trust relationships: for modeling trust, each model uses a type of trust relationships which could be local, collective or global. In local relationship user uses their own opinions to collaborate with other users. However, in the collective relationship, the user must usually take into account the opinion of his friends about whom user he will collaborate. The global relationship is based on the reputation of the user. Trust note: the note of trust between two users is either Explicit or implicit. The first one is defined directly by users, but the second one is obtained by inference on the user’s history. Trust value: trust between two users can be a binary value or gradual value which is between [0, 1]. Trust propagation: is obtained by a prediction process of trust score along a path between two users.

Trust aggregation: is a process of combining several trust scores from different paths. Trust contextualization: trust between users is strongly related to the context. It is not an absolute value: that’s to say, a user X can give a low trust to Y for the health field but it can give it a strong trust to the computer field. Table 1: Comparison of trust models AbdulRahman and Hailes (2000)

Massa and Bhattacharjee (2004)

Golbeck (2005)

O’Donovan and Smyth (2005)

Simon et al (2012)

Haydar (2014)

local

x

x

x

x

-

x

collective

-

-

-

-

x

x

global

-

-

-

x

-

x

explicit

x

x

x

-

x

x

implicit

-

-

-

x

-

-

binary

-

x

-

-

-

-

gradual

-

-

x

x

x

x

Trust propagation

-

x

x

x

x

x

Trust aggregation

-

-

x

x

x

x

Trust contextualization

x

-

-

-

-

x

-

-

-

-

-

subjective logic

similarity between users

similarity between users

-

distance between two nodes

Trust relationships

Trust note

Trust value

used theory Used technique used measure

-

Based on this comparative table, we note that: - Most of the proposed models are based on local trust. This influences the recommendation performance because the local modeling has problems related to the lack of data Haydar (2014). In another manner, the decision is made with minimal user knowledge. - For the trust propagation, we find that all research works except Abdul-Rahman and Hailes (2000), are based on the trust propagation between users. The trust propagation allows users which aren’t directly connected to be able to predict a trust score. It is expressed by a conjunctive combination.

-

- Most of research works are based on trust aggregation mechanism to estimate trust between two users. This is to combine different trust scores determined by the propagation process from different paths. To compute the final score between two users, the disjunctive combination is used. - To our knowledge, the trust contextualization is considered only in Abdul-Rahman and Hailes (2000), and Haydar (2014). Although, the model of Abdul-Rahman and Hailes (2000) is among the first models that treated this concept, but it didn’t take into account the transitivity of trust that has become important in decision making. The criterion of trust contextualization is considered in several works as essential for the quality of the decision. In our comparative study, we found that Abdul-Rahman and Hailes (2000), and Haydar (2014) take it into account. In order to define a trust model taking into account the local, collective and global trust, the author Haydar (2014) uses the Subjective Logic (LS). Indeed, the LS has a better formal framework for modeling trust. It also allows representing the relationship of trust between users in the form of probabilistic opinions. An opinion in the subjective sense represents a resulted accumulation of several interactions between the user and the object of opinion (one item or another user). However, at the modeling of opinions, this theory has two drawbacks Emmanuel et al (2008): first, it models different views in the same way, that is to say, by the principle of equal probability. On the other hand, it only models singleton opinions. Actually we are working on trust model definition which is based, on the theory of belief functions, introduced by Smets (1994), for several advantages thanks to his Transferable Belief Model. It is a richer and more flexible framework to model different ways distinct opinions. Indeed, it allows modeling of composite hypotheses, imperfect information and merging information from different resources Ahmaed (2014), Emmanuel et al (2008) and Patrick (2001). The strength of this model is manifest in the fusion of information from different sources to make an interest decisions. Furthermore, we will use formal concept analysis approaches for organization and representation of agents. The choice of formal concept analysis is justified by the sound mathematical foundation and its algorithmic branch for organizing agents in the form of groups that support the notion of collective trust. In addition, the Galois lattices allow expressing a semantic due to the partial order relation between groups Ganter and Wille (1999).

5. CONCLUSION In this paper, we presented an overview on trust-based recommendation models. We have highlighted the advantages and limitations of each model. We did this work in the objective to justify the choice of our new approach for trust-based recommendation system. In the future work we plan to implement our new model which is based on formal concept analysis for agent representation and the theory of belief function for decision making.

ACKNOWLEDGMENT For financial support, our thanks to RIADI Laboratory, ENSI, University of Manouba, Tunisia.

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