Trust-based Recommender Systems Research Articles

Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset.

Toward Trust-Based Recommender Systems for Open Data: A Literature Review

In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental concepts and illustrate the potential of using trust-based recommender systems for open data portals. We hope this literature review can benefit practitioners in the field of open data and facilitate the discussion of future work.

A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends

In recent years, the use of trust-based recommendation systems to predict the scores of items not rated by users has attracted many researchers’ interest. Accordingly, they create a trusted network of users, move in the trust graph, and search for the desired rank among the users by creating a Trust Walker and Random walk algorithm. Meanwhile, we face some challenges such as calculating the level of trust between users, the movement of Trust Walker using Random walk (random route selection), not discovering the desired rank, and as a result, the algorithm failure. In the present study, in order to solve the mentioned challenges, a trust-based recommender system is presented that predicts the ranks of items that the target user has not rated. In the first stage, a trusted network is developed based on the three criteria. In the next step, we define a Trust Walker to calculate the level of trust between users, and we apply the Biased Random Walk (BRW) algorithm to move it; the proposed method recommends it to the target user in the case of finding the desired rank of the item, and if that item does not exist in the defined trust network, it uses association rules to recognize items that are dependent on the item being searched and recommends them to the target user. The evaluation of this research has been performed on three datasets, and the obtained results indicate higher efficiency and more accuracy of the proposed method.

A genre trust model for defending shilling attacks in recommender systems

Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

Advancing on weighted PLS-SEM in examining the trust-based recommendation system in pioneering product promotion effectiveness.

The advancement in digital technologies has led to an explosive information phenomenon, particularly in Internet shopping. This paper attempts to examine the trust element in the current pervasive use of the recommendation system for product promotion effectiveness. Owing to the nature of high-volume online consumers and the nonexistence of the online consumer sampling frame, sampling weight adjustment approach was utilised for ensuring sample representativeness. Additionally, the responses collected were further analysed according to gender for a holistic understanding of the trust element. A cross-sectional quantitative research approach was adopted. Specifically, snowball sampling method was used to collect responses from online consumers. The findings revealed that benevolence, integrity, and competence trust are found to be positively associated with product promotion effectiveness. Competence trust recorded a large effect size followed by benevolence and integrity trust. Both male and female consumers shown different degrees of trust level. The findings provide practical implications for online merchants. They were suggested to focus on enhancing online consumers’ trust level and capitalize on competence trust for effective product promotion. They should also recognize the gender differences in the trust level for product promotion effectiveness when they are promoting gender-based products and services.

On deep neural network for trust aware cross domain recommendations in E-commerce

Over the years, cross-domain and trust-based recommendation systems are proven to be very helpful in solving issues pertaining to data sparsity and cold start. Many e-commerce sites used recommender systems as business tools for increasing their sale productivity and help their customers in finding suitable products. However, due to sparse rating and lack of historical information, such systems cannot generate effective recommendations. Matrix factorization and deep learning techniques have been the focus of research community for the last few years to solve the problem of data sparsity and cold start. In this paper, we have proposed a model called Trust Aware Cross-Domain Deep Neural Matrix Factorization (TCrossDNMF) that predicts rating of an item for an active user and solves user cold start problem in the cross-domain scenario of ‘User Overlap’ in e-commerce system. TCrossDNMF model is divided into four main steps: i) Features learning that learns the users’ features using a latent factor model and then finds the similarity between users of source and target domains. As the users are shared between two domains, the proposed model learns the common information and transfers the knowledge from a source to target domain. ii) Ranking that finds set of similar users (neighbors), and then filters out the dissimilar users based on similarity threshold θ, and then generates a bipartite trust graph from these reduced set of users and executes Ant Colony Optimization, to find trustworthy neighbors for an active user. iii) Weighting computes the trust degree between an active user and his or her top-k neighbors. iv) Prediction that trains the TCrossDNMF model using multilayer perceptron (MLP) and generalized matrix factorization (GMF) by representing user-item interactions in higher dimensions and ensembles the GMF and MLP with trust information for rating prediction. We evaluated proposed model on a real dataset collected from a popular e-commerce retail service ‘AliExpress’. We used categories available in ‘AliExpress’ as source domain and a target domain. For observing the performance of proposed model, we took six domains that have a higher ratio of sparsity. The proposed model is evaluated by using MAE, RMSE and F-measure metrics and compared it with baselines. The experiments show that the proposed model is a viable solution for the mentioned problem with significant improvements in results.

Multimodal trust based recommender system with machine learning approaches for movie recommendation

Recommender system (RS) are a type of suggestion to the information overload problem suffered by user of websites that allow the rating of particular item. The movie RS are one of the most efficient, useful, and widespread applications for individual to watch movie with minimum decision time. Many attempts made by the researchers to solve these issues like watching movie, purchasing book etc., through RS, whereas most of the study fails to address cold start problem, data sparsity and malicious attacks. This study address these problems, we propose trust matrix measure in this paper, which combines user similarity with weighted trust propagation. Non cold user passed through different models with trust filter and a cold user generated an optimal score with their preferences for recommendation. Four different recommendation models such as Backpropagation (BPNN) model, SVD (Singular Value Decomposition) model, DNN (Deep Neural Network model) and DNN with Trust were compared to recommend the suitable movie to the user. Results imply that DNN with trust model proved to be the best model with high accuracy of 83% with 0.74 MSE value and can be used for best movie recommendation.

Extra: Expertise-Boosted Model for Trust-Based Recommendation System Based on Supervised Random Walk

The quality of recommendations based on any class of recommender systems may become poor if no or low quality data has been provided by users.This is a situation known as Cold Start problem, which typically happens when a new user registers to the system and no preference data is available for that user. Trust-Aware Recommendation Systems can be considered as a a solution for the cold start problem. In these systems, the trust between users plays an import role for making recommendations. However, most of the Trust-Aware RSs consider trust as a context independent phenomenon which means if user a trusts user b to the degree k then user a trusts user b to the degree k in all the concepts. However, in reality, trust is context dependent and user a can trust user b in context X but not in Y . Moreover, most of the trust-aware RSs do not consider an expertise concept for users and all the users are considered as same in the recommendation process. In this paper we proposed a novel approach for detecting expert users just based on their ratings (unlike previous systems which consider the separate profile and extra information for each user to nd an expert). In this model a supervised random walk is exploited to search the trust network for finding experts. Empirical experiments on the Epinions dataset shows that EXTRA can outperform previous models in terms of accuracy and coverage.

Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. In this framework, personalized services are necessary. Recommender systems can be used in an academic environment to assist users in their teaching-learning processes. In this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. We don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. The results obtained in the experiments proved to be satisfactory.

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.

Trust circle recommended model based on different fields

A trust-based recommendation system recommends the resources needed for users by rating data and users' trust relationship. Current relevant work is usually based on over-generalised trust relationships, and the relationship between trust information and specific fields has not been fully exploited, which will affecting the precision and reliability of the recommendation. Therefore, this research proposes a users' trust circle model. It exploits potential implicit trust relationships hierarchically based on specific fields and fully merges explicit trust relationships to predicting ratings. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiment proved that the trust relationship is field-oriented, user's trust circle in different fields is different. It also proved that dividing trust relationship by field can effectively improve the precision of recommendation.

A Trust-Based Collaborative Filtering Approach to Design Recommender Systems

Collaborative Filtering (CF) is one of the most fre-quently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction. Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance. Moreover, CF is fragile and it is not suitable to find similar users. The existing literatures on CF show that integrating users’ social information with a recommender system can handle the above-mentioned issues effectively. Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items. In this paper, we propose a trust-based recommender system, TrustRER, which integrates users’ trusts into an existing user-based CF algorithm for rating prediction. It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores. For trust score, we have defined three novel trust statements based on user rating values, emotion values, and re-view helpfulness votes. To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected. The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method. TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.

Recommendation Model for Trust Circle Mining Based on Users' Interest Fields

A trust-based recommendation system recommends the resources needed for users by system rating data and users' trust relationship. In current relevant work, an over-generalized trust relationship is likely to be considered without exploiting the relationship between trust information and interest fields, affecting the precision and reliability of the recommendation. This research, therefore, proposes a users' interest-field-based trust circle model. Based on different interest fields, it exploits potential implicit trust relationships in separated layers. Besides, it conducts user rating by combining explicit trust relationships. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users do not revealed in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiments made with the Epinions data set proved that the recommendation model based on trust circle exploiting in users' interest fields proposed in this research, is able to effectively improve the precision and coverage of the recommendation rating prediction, compared with the traditional recommendation algorithm based on generalized trust relationship.

A compositional model of multi-faceted trust for personalized item recommendation

Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations as a violation of their own privacy. In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain:1. We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains. MTM flexibly integrates social links with public anonymous feedback received by user profiles and user contributions in social networks.2. We propose LOCABAL+, based on MTM, which extends the LOCABAL trust-based recommender system with multi-faceted trust and trust-based social regularization.Experiments carried out on two public datasets of item reviews show that, with a minor loss of user coverage, LOCABAL+ outperforms state-of-the art trust-based recommender systems and Collaborative Filtering in accuracy, ranking of items and error minimization both when it uses complete information about trust and when it ignores social relations. The combination of MTM with LOCABAL+ thus represents a promising alternative to state-of-the-art trust-based recommender systems.

Trust-based recommendation systems in Internet of Things: a systematic literature review

Internet of Things (IoT) creates a world where smart objects and services interacting autonomously. Taking into account the dynamic-heterogeneous characteristic of interconnected devices in IoT, demand for a trust model to guarantee security, authentication, authorization, and confidentiality of connected things, regardless of their functionality, is imperative. However, as far as we know, against the centrality of trust-based recommendation mechanisms in the IoT environment, there is no ambient study for investigating its techniques. In this paper, we present a systematic literature review (SLR) of trust based IoT recommendation techniques so far. Detailed classifications based on extracted parameters as well as investigation existing techniques in three different IoT layers put forth. Moreover, the advantages, disadvantages and open issues of each approach are introduced that can expand more frontier in obtaining accurate IoT recommendation in the future.

Detecting shilling attacks in social recommender systems based on time series analysis and trust features

In social recommender systems or trust-based recommender systems, malicious users can bias the recommendations by injecting a large number of fake profiles and by building bogus trust relationships. The existing shilling attack detection methods suffer from low precision when detecting attacks in social recommender systems because they focus mainly on the rating pattern differences between attack profiles and genuine ones and ignore the trust relationships between users. In this paper, we propose an approach for detecting shilling attacks in social recommender systems based on time series analysis and trust features (TSA–TF). Firstly, we construct rating distribution time series for items and propose a dynamic rating distribution prediction model to detect suspicious items by using a single exponential smoothing method. Then, we filter out a part of genuine user profiles by analyzing suspicious items and obtain the set of suspicious user profiles. Secondly, we propose four features by combining rating patterns and trust relationships and train a support vector machine (SVM) classifier to discriminate attack profiles in the set of suspicious user profiles. Experiments on the CiaoDVD dataset and Epinions dataset show that the proposed approach can improve the detection precision while maintaining a high recall.

Integrating the importance levels of friends into trust-based ant-colony recommender systems

PurposeThis paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.Design/methodology/approachTo obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.FindingsThe experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.Originality/valueExisting trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.

Architectural design of trust based recommendation system in customer relationship management

Most Companies are more customer centric than they were before. By adopting this strategy, it made the electronic commerce growing and enhance buyers experience. from the other side, Companies started to explore this customer experience -data generated- to extract knowledge about their customer to be well managed – eCRM - instead of classic customer relationship Management - CRM. Large quantity of Data motivated the companies to look for changes, and ask for more functionality, and this are what influenced software editors to adapt their solutions and implement the power of data. Nowadays, data available – Big Data – put the existing systems and architectures under question and push us to rethink the logical layer to explore this data. Following the data vague, puts a need to reconsider and study the strength of eCRM/CRM existing solutions and architectures. The main contribution of this paper is to propose architecture built on Trust-Based recommendation able to provide to companies better accuracy, coverage, novelty and diversity during the sales process.