Mining Heterogeneous Influence and Indirect Trust for Recommendation

Li, Zhaoyi; Xiong, Fei; Wang, Ximeng; Guan, Zhe; Chen, Hongshu

doi:10.1109/access.2020.2968102

Cited by 8 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It considers user and item bias information and user implicit feedback information predicting ratings and has high recommendation accuracy. 6) ReHI is an algorithm proposed by Z. Li et al [53].…”

Section: ) Comparison Algorithm and Hyper-parameter Settingmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

2022

View full text Add to dashboard Cite

Personalized recommendation has gained widespread attention in the academic and industrial fields to minimize information overload and has produced good benefits. Current research shows that social recommendations that effectively utilize user trust relationships can solve data sparsity and cold start problems common in traditional collaborative filtering algorithms. However, existing social recommendation models have focused only on direct trust relationships between users and have ignored indirect trust relationships and item correlations. To address these problems, we propose a probabilistic matrix factorization-based recommendation model based on trust relationships, interest mining, and item correlation. The proposed recommendation model considers the direct and indirect trust relationships between users, the similarities in users' preferences for item attributes, and the correlations between items. Finally, the rating of the item is predicted by the target user and provides the target user with personalized item recommendations. We evaluate the recommendation performances of the proposed recommendation model on the FilmTrust and the CiaoDVD datasets and find that it alleviates the user's cold start problem and provides higher recommendation accuracy and diversity than popular algorithms.

show abstract

Section: ) Comparison Algorithm and Hyper-parameter Settingmentioning

confidence: 99%

“…In view of this deficiency, Cui, Lin et al [52] proposed the DMFA-SR model, and Z. Li et al [53] proposed the ReHI model. These models use trust propagation in social networks to fully exploit indirect trust relationships between users.…”

Section: Introductionmentioning

confidence: 99%

Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

2022

View full text Add to dashboard Cite

show abstract

“…Trust is classified into implicit and explicit trust. Some studies have incorporated both implicit and explicit trusts into their RS model [37]. The implicit trust focuses on the degree of similarity between users or items and are computed either by integrating a social network trust [16,38], applying weighted similarity measures [39,40], incorporating fuzzy logic [41,42] or using probabilistic methods [43] in the RS.…”

Section: Related Workmentioning

confidence: 99%

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

Iwendi

Ibeke

Eggoni

et al. 2021

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

show abstract

“…Based on the above characteristics of successive interference cancellation techniques, this paper will specifically design different link scheduling strategies to maximize the number of data links that can be transmitted concurrently, thereby reducing broadcast delay. 6 Complexity…”

Section: Basic Eory Of Successive Interference Cancellationmentioning

confidence: 99%

“…As nodes move, two nodes can enter the communication range and exchange data [5]. Opportunistic complex networks can handle problems that are difficult to solve with existing wireless technology networks, such as network splits and delays, and can meet the network communication that requires low-cost and terrible conditions [6,7]. In fact, part of the concept of opportunistic complex networks is derived from the early research on delay tolerant network (DTN) [8].…”

Section: Introductionmentioning

confidence: 99%

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Yang

Luo

2020

Complexity

View full text Add to dashboard Cite

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

show abstract

Mining Heterogeneous Influence and Indirect Trust for Recommendation

Cited by 8 publications

References 27 publications

Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Contact Info

Product

Resources

About