Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Santhi Thilagam, P.S.Subject: search.filters.subject.Recommender system

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    Alleviating data sparsity and cold start in recommender systems using social behaviour
    (Institute of Electrical and Electronics Engineers Inc., 2016) Reshma, R.; Ambikesh, G.; Santhi Thilagam, P.S.
    Recommender systems are used to find preferences of people or to predict the ratings with the help of information available from other users. The most widely used collaborative filtering recommender system by the e-commerce sites suffers from both the sparsity and cold-start problem due to insufficient data. Most of the existing systems consider only the ratings of the similar users and they do not give any preferences to the social behavior of users which shall aid the recommendations made to the user to a great extent. In this paper, instead of finding similarity from rating information, we propose a new approach which predicts the ratings of items by considering directed and transitive trust with timestamps and profile similarity from the social network along with the user-rated information. In cases where the trust and the rating details of users from the system is absent, we still make use of the social data of the users like the products liked by the user, user's social profile-education status, location etc.To make recommendation. Experimental analysis proves that our approach can improve the user recommendations at the extreme levels of sparsity in user-rating data. We also show that our approach works considerably well for cold-start users under the circumstances where collaborative filtering approach fails. © 2016 IEEE.
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    User Interest Drift Identification Using Contextual Factors in Implicit Feedback-Based Recommender Systems
    (Springer Science and Business Media Deutschland GmbH, 2023) Chaitanya, V.S.; Deo, S.; Santhi Thilagam, P.S.
    The modeling of appropriate recommendations using the session interactions in the implicit feedback-based recommender systems necessitates the identification of user interest drift. But this identification is challenging due to the presence of unintentional interactions (noise) made by the user. Most of the existing literature focused on understanding the correlation between ongoing session interactions but did not explore the contextual factors, such as the time of occurrence of the session and the item’s popularity, that led the user to perform that specific interaction. This has resulted in the wrongful categorization of interactions between user interest drift and noise. To overcome these limitations, this work proposes a deep learning-based approach that uses both ongoing session information and contextual information. Depending on availability, this work also considers the user’s previous interactions to generate personalized recommendations. In comparison with the existing works, this work effectively identifies the user interest drift and generates the appropriate recommendations for the users. The proposed approach demonstrates superior performance over state-of-the-art baselines in terms of Recall and MRR, as evidenced by experimental results on benchmark datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.