Guibing Guo

Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.

A Novel Recommendation Model Regularized with User Trust and Item Ratings

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.

From ratings to trust: an empirical study of implicit trust in recommender systems

Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.

Factored similarity models with social trust for top-N item recommendation

Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim that social trust relationships also have an important impact on a users preference for a specific item. Experimental results on three real-world data sets demonstrate that our approach achieves superior ranking performance to other counterparts.

Leveraging prior ratings for recommender systems in e-commerce

User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users’ experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance.

Exploiting Implicit Item Relationships for Recommender Systems

Collaborative filtering inherently suffers from the data sparsity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking into account implicit item relationships. Specifically, we employ an adapted association rule technique to reveal implicit item relationships in terms of item-to-item and group-to-item associations, which are then used to regularize the generation of low-rank user- and item-feature matrices. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach against other counterparts.

A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems

User-based collaborative filtering, a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts.

Integrating Trust with User Preference for Effective Web Service Composition

Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences; (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. The existing studies concentrate on either user preference or service trust, and fail to provide a systematic solution to integrate both user preference and service trust together for service compositions. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We investigate the application of heuristic algorithms on multi-objective optimization for the service composition problem. A new hybrid nature inspired intelligent algorithm is also proposed and compared with other popular heuristic algorithms. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.

Improving PGP Web of Trust through the Expansion of Trusted Neighborhood

PGP Web of Trust where users can sign digital signatures on public key certificates of other users has been successfully applied in securing emails and files transmitted over the Internet. However, its rigorous restrictions on utilizable trust relationships and acceptable signatures limit its performance. In this paper, we first make some modification and extension to PGP Web of Trust by relaxing those constraints. In addition, we propose a novel method to further expand trusted neighborhood of users by merging the signatures of the trusted neighbors and finding the similar users based on the merged signature set. Confirmed by the experiments carried out in different simulated real-life scenarios, our method applied to both the modified and extended PGP methods can improve their performance. With the expansion of trusted neighborhood, the performance of the original PGP Web of Trust is also improved considerably.

Optimal and Effective Web Service Composition with Trust and User Preference

Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences, (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Experimental results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.