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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Cheluvegowda, Padma Muthalambikashettahally
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Computer Science & IT Engineering

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Session click sentiment behavior aware personalized recommendations system Suresh, Suraj Bevinahalli; Cheluvegowda, Padma Muthalambikashettahally
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1539-1547

Abstract

Session-based recommendations use short-term behavior of users to provide personalized suggestions to consumers in ecommerce platform. However, cold start, considering newly joined users and sparsity issues, where not enough short-term behavior is available, and the performance of traditional session-based recommendations is significantly impacted. Deep learning (DL) like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs) have been employed to capture session-clicks and enhance product recommendation accuracy. However, the current method is significantly affected due to the gradient descent problem in meeting convergence for top-K product recommendation. Further, the current method failed to capture product sentiment for session-clicks between inter-session and intra-session clicks. In addressing the research problems, the current research work introduced a session click sentiment behavior aware (SCSBA) personalized recommendation system using novel inter and intra session (IIS)-LSTM model. Finally, the objective function to recommend top K items to users is done using optimized Bayesian personalized ranking (OBPR) algorithm. Experiment outcome shows the SCSBA model achieves much better performance than state of art model, considering standard Tmall dataset.
Co-Authors Suresh, Suraj Bevinahalli