Data Augmentation Strategies for Improving Sequential Recommender Systems

Song, Joo-yeong; Suh, Bongwon

doi:10.48550/arxiv.2203.14037

Cited by 1 publication

(2 citation statements)

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“…The sequential recommender system has been recognized as an essential field for academics and researchers in the last 3-5 years. As a result, many related papers have been published or are under research in top AI related conferences, such as SIGIR, CIKM, and RecSys [84], [85], [86], [87], [88], [89], [90], [91], [92], [93]. This paper introduces sequential recommendation, which can analyze user-item interactions more accurately and flexibly through temporal factors, and explores the concepts, architecture, application, and detailed models of related representative deep learning-based recommendation models.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

“…Network compression [83], [84] is expected to be one of the important topics to be studied among researchers because it is closely related to agility, efficiency, and accuracy to improve the performance of recommender systems. Network compression is one of deep learning lightweight methods that use model reduction techniques to reduce the number of parameters, and related learning techniques include data augmentation [85], knowledge distillation [86], and transfer learning [87]. Data augmentation is a technique proposed to predict parameters well and achieve high performance of deep learning models and is used when training data is small.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

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Evolution of Deep Learning-Based Sequential Recommender Systems: From Current Trends to New Perspectives

Yoon

Jang

2023

IEEE Access

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The recommender system which gets higher in practical use in applying the Apriori algorithm in the early 2000s has revolutionized our daily life as it currently is widely used by big-tech platform companies. In the early stages of the development of recommender systems, services that can be provided to users were simply derived to the extent that only related products were recommended. However, the new research wave like deep learning-based recommender systems due to the development of information technology and the complexity of users' online behavior extensively grabs researchers' and academia's attention in the field of recommender systems. This paper describes the algorithms and characteristics of the recent popular deep learning-based representative models such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) in the view of the sequential recommendation. The sequential recommendation of understanding user preferences in chronological order is useful for analyzing user-item interaction more accurately and flexibly. Therefore, the models specialized in sequential recommendation take advantage of understanding user behavior through temporal factors and improving recommendation quality by easily realizing the correlation between user and items. Also, the transformer-based model was developed to improve the problem of long-term dependency between users and items through factors, such as points, lines, and nodes, experienced in the early models of RNN and CNN and self-supervised learning (SSL)-based models, which are originally purposed to solve the data sparsity issues of recommender systems, will be discussed in this paper.

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