An effective social recommendation method based on user reputation model and rating profile enhancement

2018 ◽  
Vol 45 (5) ◽  
pp. 607-642 ◽  
Author(s):  
Sajad Ahmadian ◽  
Mohsen Afsharchi ◽  
Majid Meghdadi
Keyword(s):  
Recommender Systems ◽  
Cold Start ◽  
Reliability Measures ◽  
Data Sparsity ◽  
Reliability Measure ◽  
Trust Networks ◽  
Reputation Model ◽  
Real World Datasets ◽  
User Reputation ◽  
Trust Information

Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through trust relations. However, the lack of sufficient trust information can reduce the efficiency of these methods. Moreover, diversity and novelty are important measures for providing more attractive suggestions to users. In this article, a reputation-based approach is proposed to improve trust-aware recommender systems by enhancing rating profiles of the users who have insufficient ratings and trust information. In particular, we use a user reliability measure to determine the effectiveness of the rating profiles and trust networks of users in predicting unseen items. Then, a novel user reputation model is introduced based on the combination of the rating profiles and trust networks. The main idea of the proposed method is to enhance the rating profiles of the users who have low user reliability measure by adding a number of virtual ratings. To this end, the proposed user reputation model is used to predict the virtual ratings. In addition, the diversity, novelty and reliability measures of items are considered in the proposed rating profile enhancement mechanism. Therefore, the proposed method can improve the recommender systems about the cold start and data sparsity problems and also the diversity, novelty and reliability measures. Experimental results based on three real-world datasets show that the proposed method achieves higher performance than other recommendation methods.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 3830-3837 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Zhiping Gu ◽  
Qingkui Chen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Cold Start ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Item Item ◽  
Cold Start Problem

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

Knowledge-Guided Disentangled Representation Learning for Recommender Systems

2022 ◽  
Vol 40 (1) ◽  
pp. 1-26
Author(s):  
Shanlei Mu ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Siqing Li ◽  
Ji-Rong Wen
Keyword(s):  
Recommender Systems ◽  
Semantic Information ◽  
Representation Learning ◽  
Interaction Data ◽  
Data Sparsity ◽  
Proposed Model ◽  
Information Maximization ◽  
Alignment Strategy ◽  
Real World Datasets ◽  
Mutual Information Maximization

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

scholarly journals Determining Trust Based Examination on Social Networks for Hotel Recommendation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 218-221 ◽  
Keyword(s):  
Social Networks ◽  
Social Trust ◽  
Research Work ◽  
Cold Start ◽  
Data Sparsity ◽  
Sparsity Problem ◽  
Implicit And Explicit ◽  
Fake Reviews ◽  
Trust Information

The objective of this research work is to enhance the performance of a novel recommendation site for tavern by mining the data about all kinds of hotel in websites. When we are about to view the rating of a hotel given by other people, we can’t rely accurately on it as there are many fake reviews. So we can overcome the problem by viewing only the ratings given by our friends and friends of friends in our trusted network. TrustSVD algorithm considered for ensuring this. By considering both the implicit and explicit opinions of ratings the predictions are made. The proposed technique is used to merge with social trust information thus from that we can get the trusted network. Thus, by using the trusted network reviews we can avoid the fake reviews. In case of cold start and data sparsity problem the friends of friends list is considered.

scholarly journals STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

2019 ◽  
Author(s):  
Jiani Zhang ◽  
Xingjian Shi ◽  
Shenglin Zhao ◽  
Irwin King
Keyword(s):  
Recommender Systems ◽  
Matrix Completion ◽  
Model Space ◽  
Cold Start ◽  
Training Strategy ◽  
Convolutional Networks ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Reconstructed Graph ◽  
Node Embeddings

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

Matrix factorization completed multicontext data for tensor-enhanced recommendation

2021 ◽  
pp. 1-12
Author(s):  
Shangju Deng ◽  
Jiwei Qin
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Matrix Factorization ◽  
Cold Start ◽  
User Preferences ◽  
High Dimensional ◽  
Satisfactory Performance ◽  
Interactive Data ◽  
Real World Datasets ◽  
Item Data

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.

scholarly journals A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Rui Chen ◽  
Qingyi Hua ◽  
Quanli Gao ◽  
Ying Xing
Keyword(s):  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Matrix Factorization ◽  
Rapid Development ◽  
Gaussian Mixture ◽  
Factorization Method ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Hybrid Recommender

Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

Sign in / Sign up

Export Citation Format

Share Document