A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems

Dang, Tran Khanh; Nguyen, Quang Phu; Nguyen, Van Sinh

doi:10.1007/s42979-020-00222-y

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…The problem of AR-based RS is that it does not personalize recommendations to users, thus recommendations are general, monotonous, thus look unrelated to the item being browsed by U. To improve this limitation, session-based RS is proposed, where the session is a virtual time-space created when a user browses a web portal URL [11]- [16]. Within this time space, the items that the user is or had been looking for, thus assumed as his/her preferences, can be temporarily recorded locally [14]- [16].…”

Section: Article Infomentioning

confidence: 99%

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Recurrent Session Approach to Generative Association Rule based Recommendation

Armanda,

Wardhani,

Akhriza

et al. 2023

Knowl. Eng. Data Sci.

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This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot. In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability.

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Section: Article Infomentioning

confidence: 99%

“…Within this time space, the items that the user is or had been looking for, thus assumed as his/her preferences, can be temporarily recorded locally [14]- [16]. Some methods use Markov chains [17]- [19], artificial neural networks [11], [20]- [22], and association rule learning approaches [23]- [28] to develop session-based RS.…”

Section: Article Infomentioning

confidence: 99%

Recurrent Session Approach to Generative Association Rule based Recommendation

Armanda,

Wardhani,

Akhriza

et al. 2023

Knowl. Eng. Data Sci.

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“…Dai et al [51] introduced a personalized recommendation algorithm for online learning resources based on an improved backpropagation neural network algorithm. Dang et al [52] discussed session-based recommendation, a recently proposed approach that reduces dependence on user profiles while maintaining high accuracy. The paper used real-world datasets and evaluation metrics to compare the performance of various session-based recommendation algorithms, including the deep learning approach named GRU4Rec.…”

Section: B Research Studymentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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“…content to users, successfully solving the problem of information overload. Traditional recommendation methods such as collaborative filtering recommendation [3], content-based recommendation [4], etc. usually rely on the availability of user profiles and long history of interactions [5], and thus may not be effective in many contemporary real-world scenarios (e.g., mobile streaming).…”

Section: Introductionmentioning

confidence: 99%

Session-based Recommendation Model of Graph Neural Network with Attention Mechanism

Jimenez,

Norton,

Salazar

et al. 2023

Preprint

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People have moved from the era of data poverty to the era of information sharing and the volume of data has exploded. Recommendation systems are based on the user's characteristics and generated behavioral data, and characterize the user's preferences through the user's ratings, comments, votes, and other behaviors; however, in many scenarios, the user's aforementioned information is not available due to security requirements. In recent years, most of the studies applied recurrent neural networks to session-based recommender systems with good results. However, these algorithms still have the following problems: 1. Modeling unidirectional transitions between consecutive items using only the sequence of user clicks. They ignore other items in the session, i.e., they ignore the complex transitions between distant items. 2. Ignoring information about the time an item is viewed. It is widely recognized that users tend to spend more time on items that interest them. In this paper, we propose a session-based recommendation model based on graph neural networks. The embedding information of items and item categories is obtained by a graph neural network. Because different features have different levels of importance in the prediction, the module uses an attention mechanism to fuse the information and get a final representation of the session that predicts the next item to interact with. Finally, the recurrent neural network (RNN) is used to obtain the session timing information for recommendation.

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A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems

Cited by 12 publications

References 20 publications

Recurrent Session Approach to Generative Association Rule based Recommendation

Recurrent Session Approach to Generative Association Rule based Recommendation

Deep Learning-Based Recommendation System: Systematic Review and Classification

Session-based Recommendation Model of Graph Neural Network with Attention Mechanism

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