CTMF: Context-Aware Trust-Based Matrix Factorization with Implicit Trust Network

2014 ◽  
Author(s):  
Jiyun Li ◽  
Caiqi Sun ◽  
Juntao Lv
Keyword(s):  
Matrix Factorization ◽  
Context Aware ◽  
Trust Network ◽  
Implicit Trust

scholarly journals Recommendation Based on Trust Diffusion Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jinfeng Yuan ◽  
Li Li
Keyword(s):  
Matrix Factorization ◽  
High Performance ◽  
Recommendation System ◽  
Online Information ◽  
Trust Network ◽  
Data Sparsity ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Sparsity Problem ◽  
Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

scholarly journals Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

2019 ◽  
Vol 9 (20) ◽  
pp. 4378 ◽  
Author(s):  
Yuan ◽  
Zahir ◽  
Yang
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
State Of The Art ◽  
Side Information ◽  
Initial Trust ◽  
The Social ◽  
Implicit Trust ◽  
Value Decomposition ◽  
Better Than

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).

scholarly journals Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Lei Guo ◽  
Yu Han ◽  
Haoran Jiang ◽  
Xinxin Yang ◽  
Xinhua Wang ◽  
...  
Keyword(s):  
Social Network ◽  
Social Influence ◽  
Matrix Factorization ◽  
Mean Squared Error ◽  
Contextual Information ◽  
Average Error ◽  
Final Decision ◽  
Context Aware ◽  
Unified Framework ◽  
Latent Factor

Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user’s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. More specifically, to explore items’ relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user’s social influence, we further integrate the user’s social network into CMF-I by sharing the user latent factor between user’s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user’s reviews, we exploit another CNN to learn user’s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).

Recommender System with Composite Social Trust Networks

2016 ◽  
Vol 13 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Chaochao Chen ◽  
Xiaolin Zheng ◽  
Mengying Zhu ◽  
Litao Xiao
Keyword(s):  
Online Social Networks ◽  
Prediction Accuracy ◽  
Social Trust ◽  
Convergence Speed ◽  
Trust Network ◽  
True Value ◽  
Trust Networks ◽  
Factorization Model ◽  
Implicit Trust ◽  
Probabilistic Matrix Factorization

The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

scholarly journals Trust information network in social Internet of things using trust-aware recommender systems

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772090877 ◽  
Author(s):  
Juyeon Son ◽  
Wonyoung Choi ◽  
Sang-Min Choi
Keyword(s):  
Internet Of Things ◽  
Recommender Systems ◽  
Trust Network ◽  
Research Issues ◽  
Context Sensitive ◽  
Implicit Trust ◽  
Social Internet Of Things ◽  
Social Link ◽  
Link Information ◽  
Trust Information

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.

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