Trust-Networks in Recommender Systems

Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset

Improving dental care recommendation systems using trust and social networks

The growing popularity of Health Social Networking sites has a tremendous impact on people's health related experiences. However, without any quality filtering, there could be a detrimental effect on the users' health. Trust-based techniques have been identified as effective methods to filter the information for recommendation systems. This research focuses on dental care related social networks and recommendation systems. Trust is critical when choosing a dental care provider due to the invasive nature of the treatment. Surprisingly, current dental care recommendation systems do not use trust-based techniques, and most of them are simple reviews and ratings sites. This research aims at improving dental care recommendation systems by proposing a new framework, taking trust into account. It derives trust from both users' social networks and from existing crowdsourced information on dental care. Such a framework could be used for other healthcare recommendation systems where trust is of major importance. © 2014 IEEE

Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction

“Do you trust me?” – A Structured Evaluation of Trust and Social Recommendation Agents

Recommender systems are considered as useful software that helps users in screening and evaluating products. The fact that users do not know how these systems make decisions leads to an information asymmetry. Thus, users need to trust if they want to take over systems’ recommendations. Applying social interfaces has been suggested as helpful extensions of recommender systems to increase trust. These are called (Social) Recommendation Agents. While many articles and implementations can be found in the field of e-commerce, we believe that Recommendation Agents can be applied to other contexts, too. However, a structured evaluation of contexts and design dimensions for Recommendation Agents is lacking. In this study, first, we give an overview of design dimensions for Recommendation Agents. Second, we explore previous research on trust and Recommendation Agents by means of a structured literature review. Finally, based on the resulting overview, we highlight three major areas for future research

TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems

Imputing Trust Network Information in NMF‐based Recommendation Systems

With the emergence of E‐commerce, recommendation system becomes a significant tool which can help both sellers and buyers. It helps sellers by increasing the profits and advertising items to customers. In addition, recommendation systems facilitate buyers to find items they are looking for easily. In recommendation systems, the rating matrix R represents users\u27 ratings for items. The rows in the rating matrix represent the users and the columns represent items. If particular user rates a particular item, then the value of the intersection of the user row and item column holds the rating value. The trust matrix T describes the trust relationship between users. The rows hold the users who create a trust relationship ‐ trustor ‐ and the columns represent users who have been trusted by trustors ‐ trustee ‐

Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy