Deep learning frameworks to learn prediction and simulation focused control system models

Tuna, Turcan; Beke, Aykut; Kumbasar, Tufan

doi:10.1007/s10489-021-02416-0

Cited by 7 publications

(1 citation statement)

References 66 publications

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“…The researchers in recommender frameworks also concentrate on algorithmic item selection and ranking methodologies. A top-N target-oriented method has been designed that uses sequence learning to capture user choice using the time domain, which also handles the cold start problem [31]. Anelli et al [32] presented improving ranking accuracy of recommendations using Neural MF.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

Awati,

Shirgave

2023

IJRITCC

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In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively.

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Section: Literature Reviewmentioning

confidence: 99%