Graph Learning based Recommender Systems: A Review

Wang, Shoujin; Hu, Liang; Wang, Yan; He, Xiangnan; Sheng, Quan Z.; Orgun, Mehmet A.; Cao, Longbing; Ricci, Francesco; Yu, Philip S.

doi:10.24963/ijcai.2021/630

Cited by 127 publications

(31 citation statements)

References 12 publications

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“…Both [12] and [13] cover the usage of knowledge graphs in RSs. Finally, [14] characterize and formalize graph learning-based RSs, their challenges, and main progress in the sub-field.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

et al. 2022

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Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets.

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“…Both [12] and [13] cover the usage of knowledge graphs in RSs. Finally, [14] characterize and formalize graph learning-based RSs, their challenges, and main progress in the sub-field.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

et al. 2022

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“…Last, we point out that some research efforts also transposed recent advances in graph representation learning [16] to recommender systems [3,47,49,50]. Especially, [50] proposed a Stacked and Reconstructed Graph Convolutional Network (STAR-GCN) architecture, extending ideas from [3] to tackle the user cold start problem from bipartite user-item graphs.…”

Section: Related Workmentioning

confidence: 99%

A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps

Briand¹,

Salha-Galvan

Bendada

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Music streaming services heavily rely on recommender systems to improve their users' experience, by helping them navigate through a large musical catalog and discover new songs, albums or artists. However, recommending relevant and personalized content to new users, with few to no interactions with the catalog, is challenging. This is commonly referred to as the user cold start problem. In this applied paper, we present the system recently deployed on the music streaming service Deezer to address this problem. The solution leverages a semi-personalized recommendation strategy, based on a deep neural network architecture and on a clustering of users from heterogeneous sources of information. We extensively show the practical impact of this system and its effectiveness at predicting the future musical preferences of cold start users on Deezer, through both offline and online large-scale experiments. Besides, we publicly release our code as well as anonymized usage data from our experiments. We hope that this release of industrial resources will benefit future research on user cold start recommendation.

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“…The key challenge of building SRSs/SBRSs lies in how to comprehensively learn the complex dependencies embedded within and between sequences/sessions to accurately infer users' timely and dynamic preferences [12]. In recent years, there has been some promising progress in tackling this challenge, including, e.g., Markov chain based approaches [10], distributed representation based approaches [13], recurrent neural network (RNN) based approaches [4], graph neural network (GNN) based approaches [15,16], reinforcement learning-based approaches [17] and contrastive learn-ing-based approaches [6].…”

Section: Introductionmentioning

confidence: 99%

Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

Wang,

Zhang,

et al. 2022

Preprint

Self Cite

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In recent years, sequential recommender systems (SRSs) and sessionbased recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical realworld applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area. CCS CONCEPTS• Information systems → Recommender systems.

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Graph Learning based Recommender Systems: A Review

Cited by 127 publications

References 12 publications

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps

Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

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