Improving graph neural network for session-based recommendation system via non-sequential interactions

Tajuddeen, Gwadabe,; Liu, Ying

doi:10.1016/j.neucom.2021.10.034

Cited by 38 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model used a graph neural network to learn nonsequential interactions first and then sequential interactions in an end-to-end manner. The model outperformed other stateof-the-art models on both Yoochoose and Diginetica datasets, with a 10% and 11% improvement in recommendation accuracy over the best-performing comparison model [8]. Roozbahani Z proposed an integrated model based on multilayer networks that can be personalized to recommend research collaborators.…”

Section: Related Workmentioning

confidence: 99%

“…Since the L1 norm is sparser than the L2 norm, while the L2 norm does not occur with most of the parameters being zero. Therefore, an adaptive coregularization equation based on L1 and L2 norms is now designed, i.e., only the L1 and L2 co-paradigms of the parameters corresponding to the non-zero input characteristics are computed and weighted as shown in equation (8).…”

Section: B Design Of Cr Models With Hybrid Adaptive Collaborative Reg...mentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of Attention-Based CR System in the Context of Big Data

Wang,

Kuang,

Lyu

2024

IEEE Access

View full text Add to dashboard Cite

The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. The publicly available datasets Criteo and Avazu from the Kaggle competition were selected for testing experiments in the study. The experimental results are as follows. During the training phase, after the convergence of each model, the loss function value of the model designed in this study is 1.26, which is the lowest among all comparison models. Moreover, when the number of recommended products is 7, the overall recommendation effect of each model is the best. The area under the curve of the subject operation characteristic curve of the model designed in this study on the Criteo dataset is 0.809, which is significantly higher than all comparison models. It is believed that this model has higher recommendation accuracy and can be used in application scenarios that require higher recommendation quality.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: B Design Of Cr Models With Hybrid Adaptive Collaborative Reg...mentioning

confidence: 99%

Design and Implementation of Attention-Based CR System in the Context of Big Data

Wang,

Kuang,

Lyu

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Figure 14 presents a relative examination of cutting-edge techniques alongside the TDF-USPRS approach using the Amazon dataset. In this evaluation, the projected TDF-USPRS technique is contrasted with two other advanced techniques, namely MP4Rec [47] and NeuACF [48].…”

Section: ) Assessing the Effectiveness Of The Tdf-usprs Technique Usi...mentioning

confidence: 99%

“…Here's a concise of the cutting-edge techniques: MP4Rec [47]: MP4Rec aims to generate top-N recommendations that are clear and interpretable. To achieve this, it employs afresh developed pair-wise objective function, combined with matrices for user-user and item-item similarity.…”

Section: ) Assessing the Effectiveness Of The Tdf-usprs Technique Usi...mentioning

confidence: 99%

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

Awati,

Shirgave

2023

IJRITCC

View full text Add to dashboard Cite

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.

show abstract

“…Ye et al [ 31 ] obtained low-dimensional representations of various entities by constructing a knowledge graph, and then input them into a neural decomposition machine for recommendation. Since traditional recurrent neural networks can only rely on linear transformations in each session to train recommendation models, Gwadabe et al [ 32 ] propose a graph neural network-based recommender system that simultaneously uses non-sequential interactions and sequential The interactive information is used for model training, which improves the model recommendation effect. To solve the data sparsity and cold-start problems, Li et al [ 33 ] investigated the construction and mining of higher-order semantic information in knowledge graphs and applied them to scholar recommendations.…”

Section: Related Workmentioning

confidence: 99%

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Yang

Li²,

Zhou³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

show abstract

Improving graph neural network for session-based recommendation system via non-sequential interactions

Cited by 38 publications

References 18 publications

Design and Implementation of Attention-Based CR System in the Context of Big Data

Design and Implementation of Attention-Based CR System in the Context of Big Data

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

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Contact Info

Product

Resources

About