A social recommender system based on reliable implicit relationships

Ahmadian, Sajad; Joorabloo, Nima; Jalili, Mahdi; Ren, Yongli; Meghdadi, Majid; Afsharchi, Mohsen

doi:10.1016/j.knosys.2019.105371

Cited by 69 publications

(32 citation statements)

References 52 publications

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“…Tahmasebi et al [21] proposed a hybrid recommendation method to address the cold start problem by using the demographic data of users. Ahmadian et al [22] developed a social recommendation system. The system calculated implicit relationships among users mathematically and introduce a new measure for evaluation of the predictions.…”

Section: Related Workmentioning

confidence: 99%

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Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Wang

Hong

2021

IEEE Access

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Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution to the problem. The method combines rating data and other types of user feedback data, such as reviews and image to improve performance of the traditional recommendation algorithms. Some researchers have proposed fusion recommendation algorithms based on BP (Back Propagation) neural network and achieved some results. However, some existing fusion recommendation algorithms based on BP neural network still have some shortcomings. They rely on the assistance of the traditional recommendation algorithms. And the high complexity of the fusion process of these algorithms would have a negative impact on the fusion effect. In this paper, we modify the fusion recommendation algorithm and propose the NNFR (neural networks fusion recommendation) model. This model improves the structure of BP neural network by specially designing the structure of network layers. User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. The fusion features of user reviews and ratings are directly applied to perform recommendation to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results show that the NNFR model can achieve better predictions than comparative recommendation algorithms to generate more accurate topk recommendation lists for users. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.

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Section: Related Workmentioning

confidence: 99%

“…According to Formula ( 19), the connection weight ℎ of the ℎth neuron in the fusion layer to the th neurons in the output layer is updated by Formula (22).…”

Section: ) Update the Connection Weightmentioning

confidence: 99%

Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Wang

Hong

2021

IEEE Access

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“…Ahmedian et al [18] proposed a social recommendation system based on implicit social relationships, they have used Dempster-Shafer theory to model the implicit relationships and introduced a new measure for unreliable predictions using neighborhood improvement mechanism. Trikha et al [24] studied the possibility of predicting the users' implicit interests based on only topic matching using frequent pattern mining without considering the semantic relatedness of the topics.…”

Section: Research Related To Collaborative Filtering Recommendation Smentioning

confidence: 99%

Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

Ghifari

Sitohang

Saptawati

2021

ITJRD

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Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However, the study still has some problems left. In the case of cold start new users, actually, the method in the study has handled it with matrix factorizer block and item weight. But it will produce the same results for all users so that the resulting personalization is still lacking. This study aims to map an overview of the themes of recommendation system research that utilizes bibliometric analysis to assess the performance of scientific articles while exposing solution opportunities to cold start problems in the recommendation system. The results of the analysis showed that cold start problems can be solved by utilizing social network data and graph approaches.

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“…In the work of [19], the authors proposed a reputation-based approach to improve trust-aware recommender systems by enhancing users' rating profiles with insufficient ratings and trust information. [20] proposed the Dempster-Shafer theory to incorporate implicit relationships to achieve the target. In the work of [21], authors used the Markov Decision Process to design a reinforcement learning model.…”

Section: Introductionmentioning

confidence: 99%

Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

Lin

et al. 2021

IEEE Access

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In recommender systems, measuring user similarity is essential for predicting a user's ratings on items. Most traditional works calculate the similarity based on historical ratings shared between two users, without considering the probability of users' different behaviors. To address this issue, our work designs a similarity measure based on the users' historical behavior probabilities. Based on the similarity method, a complex network of user relationships is modeled. The user degree and community information of the modeled network, as well as the number of shared ratings between users, are used with the proposed similarity measure to design a rating prediction algorithm for recommender systems. Experiments based on MovieLens and Netflix data sets show that this method is effective and can successfully improve the accuracy of rating predictions and reduce the number of neighbors required to achieve the optimal prediction accuracy. Our research shows that in a complex system, the relationship between users can be effectively measured by the users' historical behavior probability, providing a new perspective for future research on similarity measurement.INDEX TERMS complex network modeling, recommender systems, degree centrality, link prediction, user behavior probability.

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A social recommender system based on reliable implicit relationships

Cited by 69 publications

References 52 publications

Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

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