TA4REC: Recurrent Neural Networks with Time Attention Factors for Session-based Recommendations

Sun, Yu; Zhao, Peize; Zhang, Honggang

doi:10.1109/ijcnn.2018.8489591

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al used a hybrid global-local encoder with attention mechanisms to capture the main purpose in the session [12]. The dwell time on an item is also considered to adjust the attention weights of previous clicks in the session [11]. Also, combination-based models were proposed to improve the performance [10,37].…”

Section: Session-based Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Long-Tail Session-based Recommendation from Calibration

Chen¹,

Wang²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Accurate prediction in session-based recommendation has progressed, but a few studies focused on skewed recommendation lists caused by popularity bias. Existing models on mitigating the popularity bias attempted to reduce the over-concentration on popular items by simply amplifying scores of less popular items. However, they normally ignore the users' different preferences towards tail items. To this end, we incorporate calibration, where calibrated recommendations reflect the user's interests with appropriate proportions, to mitigate the popularity bias from the user's perspective. Concretely, we add a calibration module to predict the ratio of tail items in the recommendation list from the session representation, and align it to the ongoing session. Meanwhile, we propose a two-stage curriculum training strategy to improve the prediction in the calibration module. Experiments on benchmark datasets show that our model can both achieve the competitive accuracy of recommendation and provide more tail items.

show abstract

Section: Session-based Recommendationmentioning

confidence: 99%

“…In recent years, with the development of deep learning-based technologies, SBR has achieved satisfying prediction accuracy. In general, methods that rely on Recurrent Neural Networks (RNNs) [7,[10][11][12][13] and Graph Neural Networks (GNNs) [14][15][16][17][18] mainly contribute to the progress in SBR.…”

Section: Introductionmentioning

confidence: 99%

Long-Tail Session-based Recommendation from Calibration

Chen¹,

Wang²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Tan et al [80] deals content information in preprocessing step to reduce the effect of outdated features and the resultant subset is processed by the second model. Session based RecSyss have implemented effectively with GRU [85]. Recently contexts have also considered as input along with the sequence data [86][87][88][89].…”

Section: Gru For Recsys Modelsmentioning

confidence: 99%

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

Kala¹,

Nandhini²

2020

KSII TIIS

View full text Add to dashboard Cite

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, moviewatching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

show abstract

Deep Discriminative Session-Based Recommender System

Ravanmehr,

Mohamadrezaei

2023

Session-Based Recommender Systems Using Deep Learning

View full text Add to dashboard Cite

TA4REC: Recurrent Neural Networks with Time Attention Factors for Session-based Recommendations

Cited by 8 publications

References 21 publications

Long-Tail Session-based Recommendation from Calibration

Long-Tail Session-based Recommendation from Calibration

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

Deep Discriminative Session-Based Recommender System

Contact Info

Product

Resources

About