Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Guo, Lei; Wen, Yufei; Wang, Xinhua

doi:10.1007/s11390-018-1849-9

Cited by 27 publications

(20 citation statements)

References 51 publications

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“…Homophily theory indicates that the preference of a user is similar to or influenced by their socially connected friends ( McPherson et al, 2001 ). Similar to the KG-based recommendation, many social recommender systems seek to integrate the pre-trained social network embeddings, which indicates the degree that a user is influenced by his/her friends ( Guo et al, 2018 ; Wen et al, 2018 ; Zhang et al, 2018 ; Sathish et al, 2019 ; Chen et al, 2019a , Chen et al, 2019b ).…”

Section: Feature-based Modelsmentioning

confidence: 99%

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

et al. 2021

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Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

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Section: Feature-based Modelsmentioning

confidence: 99%

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

et al. 2021

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“…If the position between POIs is too far or the last check-in time of users is too long, it is not meaningful to use this continuity effect. Guo et al [19] first preprocessed the embedded model based on neural network to mine the deep information in social network and evaluation, and then embedded the pre-trained network into the weighted matrix decomposition method, linearly integrated the extracted factors and potential factors, and formed the collaborative filtering model which was considered more suitable for social recommendation.…”

Section: B Recommendation Of the Points-of-interestmentioning

confidence: 99%

Points-of-Interest Recommendation Algorithm Based on LBSN in Edge Computing Environment

et al. 2020

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With the advancement of the Internet of Everything era and the popularity of mobile devices, Location-based Social Networks (LBSN) have penetrated people's lives. People can take advantage of portable edge terminal devices and use the geographic information in LBSN to arrange or adjust their travel plans. However, due to the explosive growth of current Internet applications and users, it has brought greater pressure and operation and maintenance costs to cloud storage. It is a key research direction based on location recommendation to accurately obtain the places of interest of users and push them to clients in such a large amount of original data. In order to better process the data generated by edge devices, this paper firstly uses the Rank-FBPR matrix decomposition framework based on social network to analyze the user's personal preference function on the edge server. Then interact with the geographic information stored in the Cloud to cluster the POIs. And embeds the geographic information into the framework to get the candidate points of interest. Finally, the scores of candidate points of interest are predicted using the personal preference function and power law distribution, then a sorted list of points of interest is generated in descending order of scores, and the list is recommended to the target user. This algorithm effectively integrates the time information and geographic information of users' check-in in the LBSN, and proposes a POIs recommendation algorithm that comprehensively considers edge devices and Cloud. The experiments verify the effectiveness of framework from both cold start and non-cold start. The experimental results on the Foursquare and the Yelp datasets show that Rank-FBPR has higher recommendation accuracy and recall than other comparison models, and can adapt to cold start problems.

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“…With the application of this hybrid method, reliability and accuracy of the system was found to be improved significantly. To enhance recommender system, user item rating matrix was utilized in [20] to obtain social connections between rating patterns to enhance recommendation.…”

Section: Related Workmentioning

confidence: 99%

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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Recommender systems are in recent past becoming more notable in the age of swift evolution of the information technology ensuring e-commerce users' suitable items. In recent years, various thread-based and trust based approaches have been proposed, which improve the precision and recall of recommendation to certain extent. However, these approaches are less accurate than expected when users' preferences evolve over time and when several aspects of the users are involved. To solve these problems, we propose a novel recommendation method called, Soft Cosine Gradient and Gaussian Joint Probability (SCG-GJP) for Online Social Networks (OSN). Specifically, the Local Soft Cosine Clique Clustering (LSCCC) is first used to identify good candidate items to be recommended. Second, using adjacency matrix factorization, probable past preference of individual user and overall preference of whole community is formed to reduce the problem of mean absolute error. Again, the obtained matrix is trained through the stochastic function gradient to rank the most suitable candidates. Finally, Gaussian Mixture Joint Probability Postfiltering model is proposed to provide useful recommendations. The experiments are executed with real-world dataset (DS) and compared with the conventional recommendation methods. Experimental results demonstrate that the SCG-GJP obtains higher accuracy, and lesser error as well as complexity.

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Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Cited by 27 publications

References 51 publications

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

Points-of-Interest Recommendation Algorithm Based on LBSN in Edge Computing Environment

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

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