SDBPR: Social distance-aware Bayesian personalized ranking for recommendation

Zhao, Feng; Shen, Yu; Gui, Xiangyu; Jin, Hai

doi:10.1016/j.future.2018.12.052

Cited by 12 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, based on the social network among users, document [3] used the context aware Point-of-Interest (POI) recommendation model based on matrix decomposition and used the social network data of users and the POI category information to establish a general matrix decomposition model to improve the accuracy of recommendation results. Documents [4], [5] used the random walk method to travel a social network. According to the distance between users in the travel process, it predicted the similarity between users and filled in the missing values in a matrix, which fixed the data sparsity.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Considering the low satisfaction rate and low efficiency of product service plans, a personalized product service plan recommendation method adopting the degree of trust and cloud model is proposed. The recommendation algorithm mainly includes calculating the similarity and the prediction results of the scheme. First, by fully considering the user's subjective characteristics, the user's trust is used to improve the traditional similarity. Second, considering data sparsity and discreteness, the cloud drop distance similarity calculation method is introduced in the process of calculating the trust similarity, and a new similarity is generated via the weighted synthesis to predict and fill in the gaps in the data. Then, when the new user does not have the cold start problem caused by the historical score record, the neural network method can be used to classify the users based on the user characteristics. The method is proposed and introduced to predict sparse user interest features and obtain similar user sets based on feature classification. The corresponding program offers recommendations. Finally, the effectiveness and rationality of the proposed method are verified by using the recommendations for a machine tool product for a manufacturing enterprise as an example. INDEX TERMS Sparsity, cold start, user trust, cloud drop distance similarity.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The two user transmission paths are not limited to being one way. Therefore, the overall indirect trust of a user is calculated as shown in formula (5).…”

Section: B Sparse Data Filling Based On Trust Cloud Similaritymentioning

confidence: 99%

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Bayesian Personalized Ranking(BPR) is such a sorting algorithm is suitable for our case. BPR is an optimization framework that uses stochastic gradient descent to achieve pairwise sorting [41]. BPR algorithm belongs to pairwise approach, and is optimized for each user's product preferences.…”

Section: Personalized Ranking Of Poismentioning

confidence: 99%

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

Meng

Fang

2020

IEEE Access

View full text Add to dashboard Cite

“…To overcome content-based methods limitations, CF-based approaches lay on profiles of a group of users who share the same or similar tastes. CF-based methods are built on the assumption that within a group of users with preferences identical or similar to those of a target user, the historical data of some of them can be used to predict the future interests of the target user [5]. CF-based approaches are very popular and organized into memory-based CF and model-based CF [6].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Inference Based Hybrid Recommender System

2020

View full text Add to dashboard Cite

The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of modelbased methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.

show abstract

SDBPR: Social distance-aware Bayesian personalized ranking for recommendation

Cited by 12 publications

References 16 publications

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

A Bayesian Inference Based Hybrid Recommender System

Contact Info

Product

Resources

About