Evaluating Session-Based Recommendation Approaches on Datasets from Different Domains

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

doi:10.1007/978-3-030-35653-8_37

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…We created another performance comparison experiment between GRU4REC and BERT4REC on a new dataset obtained from the same provider of TMALL and IJCAI16 named TAOBAO17 6 which contains 1.4 million items after preprocessed. In this experiment, we apply the sampling strategy with the threshold of 1% as proposed in [25] due to the limited computational capacity of the BERT4REC model. Evaluation Metrics Since people tend to go online on small screen devices recently, i.e., smartphone, the shorter recommended list would be better.…”

Section: Resultsmentioning

confidence: 99%

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

Dang

Nguyen

2020

SN COMPUT. SCI.

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Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.

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Section: Resultsmentioning

confidence: 99%

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

Dang

Nguyen

2020

SN COMPUT. SCI.

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“…We continue to improve the credit card fraud detection system through other different ML and deep learning approaches. In addition, extending and applying ML techniques over highly imbalanced datasets to other application domains, like big data sampling and clustering [45], recommendation systems [46], and security and privacy issues [47] with deep learning, is also of great interest to us in the future. Additionally, we consider using the Leave-One-Out Cross-Validation approach rather than the train/test split using a ratio of 7:3 to enhance the robust performance of detecting CCF using ML algorithms [48].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

et al. 2021

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The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud and non-fraud labels. Based on two resampling approaches, SMOTE and ADASYN are used to resample the imbalanced CCF dataset. ML algorithms are, then, applied to this balanced dataset to establish CCF detection systems. Next, DRL is employed to create detection systems based on the imbalanced CCF dataset. The diverse classification metrics are indicated to thoroughly evaluate the performance of these ML and DRL models. Through empirical experiments, we identify the reliable degree of ML models based on two resampling approaches and DRL models for CCF detection. When SMOTE and ADASYN are used to resampling original CCF datasets before training/test split, the ML models show very high outcomes of above 99% accuracy. However, when these techniques are employed to resample for only the training CCF datasets, these ML models show lower results, particularly in terms of logistic regression with 1.81% precision and 3.55% F1 score for using ADASYN. Our work reveals the DRL model is ineffective and achieves low performance, with only 34.8% accuracy.

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“…• Tmall: The e-commerce data was gathered utilizing interactions with the Tmall.com website for the previous years. For Tmall, the number of events in a day as a whole was considered while setting event session timestamps [90]. Between May and November 2015, this dataset documents user activity on the Tmall e-commerce platform, including actions like viewing, buying, adding to a cart, and adding favorites.…”

Section: Aucmentioning

confidence: 99%

Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions

Elzeheiry¹,

Gab-Allah²,

Mekky³

et al. 2023

Preprint

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Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

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Evaluating Session-Based Recommendation Approaches on Datasets from Different Domains

Cited by 7 publications

References 10 publications

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

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

Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions

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