Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item PRIDES: A Probabilistic Model for Recurrent User Interest Drift Identification in Session-Based Recommendation(Institute of Electrical and Electronics Engineers Inc., 2025) Chaitanya, V.S.; Santhi Thilagam, P.S.A Session-Based Recommendation (SBR) identifies correlations among session interactions to understand user preferences and generate appropriate recommendations. A key challenge in this context is the dynamic change in user preferences, particularly when preferences disappear and reappear within a session, a phenomenon referred to as Recurrent User Interest Drift (RUID). Effectively capturing RUID is significant for aligning recommendations with ongoing user preferences. Existing SBR approaches often misclassify user preferences that differ from other session interactions as noise (unintentional interactions), relying on dwell time (the amount of time a user spends viewing an item) or neighboring sessions, thereby overlooking their potential reappearance as RUID later in the session. To the best of our knowledge, this work is the first to address the challenge of identifying RUID in SBR. The proposed approach assigns probabilistic scores to each interaction by considering its similarity to the immediate previous interaction, its inclusion among popular items (items with a higher number of interactions), its similarity to previous interactions, and the dwell time. As user preference may reappear anytime during the session, and RUID identification requires analyzing subsequent interactions, a list-based approach is used to retain these interactions until the session ends, enabling effective RUID identification. The matrix factorization-based attentive session encoder incorporates both short-term (ongoing) preferences and long-term (historical) preferences to generate personalized recommendations. Experimental results on three benchmark datasets, Yoochoose, Last.fm, and Gowalla, show that our method outperforms 14 state-of-the-art baselines, achieving an improvement of 2.28% in recall@20 and 1.39% in Mean Reciprocal Rank (MRR@20) on Yoochoose, 3.58% in recall@20 and 2.70% in MRR@20 on Last.fm, and 5.35% in recall@20 and 4.17% in MRR@20 on Gowalla datasets. © 2013 IEEE.Item Understanding dynamic user preferences using user-centric reservoir model for enhancing personalization(Springer Science and Business Media B.V., 2025) Chaitanya, V.S.; Jain, H.; Santhi Thilagam, P.S.Streaming Session-based Recommender Systems (SSRS) generate personalized recommendations from continuously arriving session data in the form of clickstreams. However, the massive arrival of clickstreams causes the forgetting of learned user preferences, necessitating periodic retraining of the SSRS model. This requires storing clickstreams in the reservoir; however, its limited storage space causes existing approaches to randomly replace and sample clickstreams, leading to the loss of user preferences that are unique compared to other users. Moreover, retraining the SSRS model with all the clickstreams in the reservoir results in an overload scenario. This work introduces a user-based partitioning approach that allocates dedicated reservoir partitions for individual user interactions. Furthermore, a priority-based sampling approach is introduced to select clickstreams from each partition for retraining. By considering both historical and ongoing user preferences, the proposed methodology generates personalized recommendations for the users. Experimental results on benchmark datasets demonstrate its superiority over state-of-the-art baseline methodologies. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.