Conference Papers

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    Comparative Performance Evaluation of Web-Based Book Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bhat, S.S.; Pranav, P.; Shashank, K.V.; Raghunandan, A.; Mohan, B.R.
    In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.
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    A Clustering-based model for the Generation of Diversified Recommendations
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chaitanya, V.S.; Mohan, M.; Santhi Thilagam, P.S.
    The primary goal of a recommender system is to generate accurate recommendations according to the user's interests. But the user's satisfaction increases when they get a chance to view the diverse categories of items. There exist several works on the generation of diverse recommendations but the performance of these methods often gets limited due to the issues such as cold start, filter bubble long tail, and grey sheep. Moreover, these methods do not consider the user's preference regarding exploration and exploitation while generating the recommendations. To this extent, this work proposes a model known as the iterative clustering-based diversity model, which can generate diverse recommendations and also solve the above-said issues. It groups the items based on the item description using the TF-IDF algorithm. The model generates two recommendations in such a way that one recommendation is similar and the other is different in comparison with the last interaction made by the user. The model has been evaluated on the benchmark dataset and has achieved promising results. © 2022 IEEE.