Exploiting deep transformer models in textual review based recommender systems

Gheewala, Shivangi; Xu, Shuxiang; Yeom, Soonja; Maqsood, Sumbal

doi:10.1016/j.eswa.2023.121120

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…Several efforts are ongoing to improve the performance of transformer models in terms of efficiency, robustness, interpretability and adaptability (OpenAI, 2018). Recently, Transformer models have been adapted to other areas of research like; recommendation system (Nikzad Khasmakhi et al, 2020;Wei et al, 2023;Gheewala et al, 2024), reinforcement learning (Parisotto et al, 2020;Chen et al, 2021b;Melo, 2022) and computer vision (Aouayeb et al, 2021;Mehta et al, 2023), with the sole goal of resolving long term challenges in different environment (Han et al, 2023).…”

Section: Recent Advancements and Future Directionsmentioning

confidence: 99%

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Ekundayo,

Ezugwu

2024

Preprint

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Deep learning stands at the forefront of contemporary machine learning techniques and is well-known for its outstanding predictive accuracy, adaptability to data variability, and remarkable ability to generalize across diverse domains. These attributes have spurred rapid progress and the emergence of novel iterations within the discipline. Yet, this swift evolution often obscures the foundational breakthroughs, with even trailblazing researchers at risk of fading into obscurity despite their seminal contributions. This study aims to provide a historical narrative of deep learning, tracing its origins from the cybernetic era to its current state-of-the-art status. We critically examine the contributions of individual pioneer scholars who have profoundly influenced the development of deep neural networks under the taxonomy of supervised, unsupervised, and reinforcement learning. Furthermore, the study also discusses the trending deep neural network architectures, explaining their operational principles, confronting associated challenges, exploring real-world applications, and outlining potential future trajectories that could offer a starting point for aspiring researchers in the field.

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Section: Recent Advancements and Future Directionsmentioning

confidence: 99%

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Ekundayo,

Ezugwu

2024

Preprint

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“…The research titled Exploiting deep transformer models in textual review-based recommender systems(Gheewala, Xu, Yeom, & Maqsood, 2024) underscores that textual reviews contain pertinent information that can effectively infer user preferences over items. The study highlights that deep learning models better capture user-item interactions from textual reviews compared to traditional recommendation approaches, thereby enhancing predictive performance.…”

mentioning

confidence: 99%

A Transformer-Based Multi-Domain Recommender System for E-commerce

Morales-Murillo,

Pinto,

Perez-Tellez

et al. 2024

Int. Journal of COP and Infor.

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Recommender systems are one of the most critical applications of AI, data science, and advanced analytics techniques because it has become integrated into our daily lives. Additionally, it serves as a powerful tool for making informed, effective, and efficient decisions and choices across a wide range of items. However, traditional techniques such as content-based and collaborative filtering often fail to consider the dynamic and short-term preferences of users when generating recommendations. To address this limitation, this research focuses on a session-based recommendation task using an XLNet transformer with various training strategies based on language modeling. Moreover, a dataset containing 102 million reviews of Amazon products was pre-processed to create two new datasets, one for a single domain and another for multi-domain data. A comparison between a GRU and the training strategies of XLNet reveals that the best training strategy achieves a 136.23% improvement in NDCG@20 and a 95.69% increase in Recall@20 for multi-domain data. In a single domain, it achieves a 168.81% improvement in NDCG@20 and a 25% increase in Recall@10.

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Rating Distribution-Aware Deep Cognitive Convolution Matrix Factorization for Recommendation Systems

Jain,

Nagar

et al. 2024

Arab J Sci Eng

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Exploiting deep transformer models in textual review based recommender systems

Cited by 7 publications

References 66 publications

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

A Transformer-Based Multi-Domain Recommender System for E-commerce

Rating Distribution-Aware Deep Cognitive Convolution Matrix Factorization for Recommendation Systems

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