A Novel Recommendation Model Regularized with User Trust and Item Ratings

Guo, Guibing; Zhang, Jie; Yorke-Smith, Neil

doi:10.1109/tkde.2016.2528249

Cited by 194 publications

(117 citation statements)

References 29 publications

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“…At each time, we consider a different part as the testing data and the remaining k − 1 parts as the training data. This method was used in [56], [30]. We compared the performance of our DNN models with the performance metrics presented in corresponding papers.…”

Section: Methodsmentioning

confidence: 99%

“…As suggested by Guha et al [14], if Alice distrusts Bob, Alice could deny all the judgments made by Bob, even if these two users are personally similar. It is well known in social recommendation research that the decisions of users are influenced by their social status and relationship [6], [10], [30].…”

Section: Related Workmentioning

confidence: 99%

“…RMSE punishes the large error more than MAE, therefore RMSE is more suitable for measuring the performance of recommender systems [30]. However, we present both metrics for our results for a comparison with existing studies.…”

Section: Metricsmentioning

confidence: 99%

“…Year Metric Datasets mTrust [33] 2012 RMSE Epinions2, Ciao DynFluid [56] 2015 RMSE Epinions2, Ciao eTrustRec [58] 2015 RMSE Epinions2 DLMF [9] 2016 RMSE Epinions1 Davoudi [7] 2016 RMSE, MAE Epinions2 Costa [59] 2016 RMSE Epinions2, Ciao TrustSVD [30] 2016 RMSE, MAE Epinions1, Ciao PTPMF [8] 2017 RMSE, MAE Epinions1 MF-NTPR [39] 2017 RMSE Epinions2 Furthermore, we compare our solution with two popular baseline models:…”

Section: Solutionmentioning

confidence: 99%

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dTrust: A Simple Deep Learning Approach for Social Recommendation

Dang

Ignat

2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. dTrust: a simple deep learning approach for social recommendationQuang-Vinh Dang, Claudia-Lavinia IgnatTo cite this version: Abstract-Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationships. However, these prediction approaches mostly rely on user personal information which is a privacy threat.In this paper, we present dTrust, a simple social recommendation approach that avoids using user personal information. It relies uniquely on the topology of an anonymized trust-useritem network that combines user trust relations with user rating scores. This topology is fed into a deep feed-forward neural network. Experiments on real-world data sets showed that dTrust outperforms state-of-the-art in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores for both warmstart and cold-start problems.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

Section: Solutionmentioning

confidence: 99%

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dTrust: A Simple Deep Learning Approach for Social Recommendation

Dang

Ignat

2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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“…Ma et al [15] introduce a novel social recommendation framework fusing the user-item matrix with users' social trust networks using probabilistic matrix factorization. Guo et al [16] propose a trust-based matrix factorization approach, TrustSVD, which takes both implicit influence of ratings and trust into consideration in order to improve the recommendation performance and at the same time to reduce the effect of the data sparsity and cold start problems. User and item side information is also a popular information source for incorporation into CF models in the form of tags [17,18], user reviews [19,20], and so on.…”

Section: Collaborative Filtering With Additionalmentioning

confidence: 99%

A Hybrid Service Recommendation Prototype Adapted for the UCWW: A Smart-City Orientation

Zhang

Ганчев

Nikolov

et al. 2017

Wireless Communications and Mobile Computing

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With the development of ubiquitous computing, recommendation systems have become essential tools in assisting users in discovering services they would find interesting. This process is highly dynamic with an increasing number of services, distributed over networks, bringing the problems of cold start and sparsity for service recommendation to a new level. To alleviate these problems, this paper proposes a hybrid service recommendation prototype utilizing user and item side information, which naturally constitute a heterogeneous information network (HIN) for use in the emerging ubiquitous consumer wireless world (UCWW) wireless communication environment that offers a consumer-centric and network-independent service operation model and allows the accomplishment of a broad range of smart-city scenarios, aiming at providing consumers with the "best" service instances that match their dynamic, contextualized, and personalized requirements and expectations. A layered architecture for the proposed prototype is described. Two recommendation models defined at both global and personalized level are proposed, with model learning based on the Bayesian Personalized Ranking (BPR). A subset of the Yelp dataset is utilized to simulate UCWW data and evaluate the proposed models. Empirical studies show that the proposed recommendation models outperform several widely deployed recommendation approaches.

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