Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Hu, Qintai; Han, Zhongmei; Lin, Xiaoyue; Huang, Qionghao; Zhang, Xiaomei

doi:10.1016/j.ins.2018.12.003

Cited by 22 publications

(10 citation statements)

References 31 publications

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“…Most of the research experiments using collaborative filtering for music recommendation are used as a proof of the universality of the recommendation method proposed. e music industry often does not have enough rating data, and collaborative filtering can cause cold starts [22,23]. In recent years, some scholars use collaborative filtering combined with other information or mixed with other methods to build a music recommendation system, which has attracted more attention.…”

Section: Research Status Of Music Recommendation Systemmentioning

confidence: 99%

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Gao

2022

Mathematical Problems in Engineering

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It is one of the main goals of personalized music recommendation system that how to accurately recommend the songs in line with users’ interests in the huge music library. In view of the above problems, this study proposes a personalized music recommendation method based on convolutional neural network. First, this study defines a training set containing potential musical characteristics and, combined with the depth of the belief network, design a music information prediction model and the research in the music-type classification method with different dimensions. Based on selecting four different kinds of music information better describing the underlying characteristics of 40D feature vector to every song music composition, the music feature set is constructed. Then, the CNN (convolutional neural network), which is widely used in audio field, is used as the music information prediction model, and its structural parameters are redesigned to complete the multidimensional music information prediction, which solves the cold start problem to some extent.

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Section: Research Status Of Music Recommendation Systemmentioning

confidence: 99%

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Gao

2022

Mathematical Problems in Engineering

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“…Graph [11] Multimedia objects, concepts Multimedia annotation [12] Images, users, and tags Link-based similarity Bipartite [13] Users and contents Influence diffusion [14] Users and contents Social recommendation Tripartite [15] Users, tags, and images Recommendation [16] Users, interaction behavior, and tags Recommendation [17] Users, Tweets, and topics Coronavirus analysis Hypergraph [18] Users, tags, and resources Consensus maximization [19] Users, time, and POIs Location prediction [20] Users and items Recommendation…”

Section: Entities Applicationmentioning

confidence: 99%

“…Zhang et al [15] presented a recommendation method using a user-image-tag model, whose main novelties concern user preference identification on the basis of users' interaction with images and re-ranking social images on the basis of the content. In [16], the authors introduced an interaction tripartite graph, composed of heterogeneous vertices (users, interaction behavior, and content), whose edge weights are tuned by using an attention-driven CNN for recommendation. A tripartite graph-whose set of vertices is composed of users, tweets, and topics-was detailed by Liao, Zheng, and Cao [17] for providing coronavirus pandemic analysis through nonnegative matrix factorization and sentiment analysis.…”

Section: Entities Applicationmentioning

confidence: 99%

Deep Learning-Based Community Detection Approach on Multimedia Social Networks

Ferraro¹,

Moscato

Sperlí

2021

Applied Sciences

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Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.

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“…In addition, there are some deep learning methods that have been proposed to improve the performance of the recommendation [32]- [35], which is closely relevant to our approach. Although these methods provide new ideas for recommendation systems from different perspectives, the disadvantages are that many parameters need to be learned, especially when the data are sparse, its ability to normalize is very weak.…”

Section: Deep Learning Modelmentioning

confidence: 99%

A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

Liu

Ren

2019

IEEE Access

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Recommender systems can provide users with an ordered list of various items, which greatly assists users to purchase products that they are satisfied with. However, item recommendation has been confronted with some inherent problems, such as sparse ratings and long-tail distribution, resulting in low accuracy of recommendations and insignificant marketing. In this paper, we propose a novel learning model based on trust diffusion and global item (TDGIL) to improve the accuracy of item rating prediction for recommender systems. Specifically, first, the rating information on items is mined and aggregated to the greatest extent based on trust diffusion characteristics among users. The benchmark prediction of item recommendation is updated by a user trust neighbor set and its item ratings, which are obtained by a trust diffusion algorithm. Then, the difference weights and compensation coefficients for all items are defined to learn users' potential preferences in the proposed global item model. Finally, the TDGIL learning algorithm is presented to train and learn the target networks by random gradient descent. The extensive experiments and results on two real-world datasets demonstrated that our proposed model can achieve significant improvements in the accuracy of rating prediction compared with some state-of-the-art methods.

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Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Cited by 22 publications

References 31 publications

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Deep Learning-Based Community Detection Approach on Multimedia Social Networks

A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

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