dTrust: A Simple Deep Learning Approach for Social Recommendation

Dang, Quang-Vinh; Ignat, Claudia-Lavinia

doi:10.1109/cic.2017.00036

Cited by 22 publications

(11 citation statements)

References 44 publications

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“…Also, privacy is one of the issues in social recommender systems as these systems rely on user personal details. To address this problem, Dang et al [66] propose a rating prediction approach called "dTrust" that exploits the topology of trust-user-item network. This approach uses deep feedforward neural network to combine user relations and user-item ratings to predict ratings.…”

Section: Miscellaneousmentioning

confidence: 99%

“…Moreover, deep learning has also opened the doors for improving the accuracy of social recommender systems. [19], [37], [44], [46], [52] 2016 17 [16], [21], [24], [25], [28], [36], [40], [41], [45], [47], [57], [60], [61], [69], [70], [65], [71] 2017 20 [17], [18], [20], [22], [26], [27], [38], [42], [43], [51], [53], [62]- [64], [66], [68], [72]- [74], [78] VI. CONCLUSION A voluminous research has been done and is also proceeding in recommender systems using deep learning.…”

Section: Miscellaneousmentioning

confidence: 99%

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An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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Section: Miscellaneousmentioning

confidence: 99%

Section: Miscellaneousmentioning

confidence: 99%

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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“…Deep learning for recommender systems becomes popular research topic in 2016 during ACM RecSys . The reasons for integrating deep learning with recommender systems are the ability of deep learning to build complex non linear relationship in input and output data, better scalability for large scale of data and better analysing incorrect label data [15]. Researchers have observed that integrating these techniques improves recommendation in tremendous manner.…”

Section: Deep Learning Based Recommendationmentioning

confidence: 99%

“…Multilayer perceptron can be applied on user-item ratings to improve recommender systems. It is the simplest model [14] .It can approximate any function [15]. The advantage of using this model is that data need not be input separately as it is directly be used in multilayer neural network model.…”

Section: Deep Learning Based Recommendationmentioning

confidence: 99%

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

Bathla¹,

Rani²,

Aggarwal³

2018

ijacsa

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Recommendation is very crucial technique for social networking sites and business organizations. It provides suggestions based on users' personalized interest and provide users with movies, books and topics links that would be most suitable for them. It can improve user effectiveness and business revenue by approximately 30%, if analyzed in intelligent manner. Social recommendation systems for traditional datasets are already analyzed by researchers and practitioners in detail. Several researchers have improved recommendation accuracy and throughput by using various innovative approaches. Deep learning has been proven to provide significant improvements in image processing and object recognition. It is machine learning technique where hidden layers are used to improve outcome. In traditional recommendation techniques, sparsity and cold start are limitations which are due to less user-item interactions. This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications. Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques. When these recommendation systems are deployed on large scale of data, accuracy degrades significantly. Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) are used as evaluation parameters which are used to prove better recommendation accuracy. Epinions, MovieLens and FilmTrust datasets are also shown as most commonly used datasets for recommendation purpose.

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“…In most popular signed directed social networks, the signs of links represent trust (positive) or distrust (negative) relationships between users [6]. Studies [7], [8] showed that trust relations play an important role in predicting user behaviors in e-commerce.…”

Section: Introductionmentioning

confidence: 99%

Link-Sign Prediction in Dynamic Signed Directed Networks

Dang

Ignat

2018

2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)

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Many real-world applications can be modeled as signed directed graphs wherein the links between nodes can have either positive or negative signs. Social networks can be modeled as signed directed graphs where positive/negative links represent trust/distrust relationships between users. In order to predict user behavior in social networks, several studies have addressed the link-sign prediction problem that predicts a link sign as positive or negative. However, the existing approaches do not take into account the time when the links were added which plays an important role in understanding the user relationships. Moreover, most of the existing approaches require the complete network information which is not realistic in modern social networks. Last but not least, these approaches are not adapted for dynamic networks and the link-sign prediction algorithms have to be reapplied each time the network changes. In this paper, we study the problem of link-sign prediction by combining random walks for graph sampling, Doc2Vec for node vectorization and Recurrent Neural Networks for prediction. The approach requires only local information and can be trained incrementally. Our experiments on the same datasets as state-ofthe-art approaches show an improved prediction. Index Terms-link-sign prediction, dynamic networks, recurrent neural networks, random walks, Doc2Vec • Epinions 1 is a product-rating social network [9]. Users of Epinions can write reviews and rate products. They also

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dTrust: A Simple Deep Learning Approach for Social Recommendation

Cited by 22 publications

References 44 publications

An Application-oriented Review of Deep Learning in Recommender Systems

An Application-oriented Review of Deep Learning in Recommender Systems

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

Link-Sign Prediction in Dynamic Signed Directed Networks

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