• Resumo

    A CARS of Novels with Imbalanced Sample Treatment

    Data de publicação: 03/05/2023

    Widely spread, recommender systems might face some challenges
    such as overspecialization and lack of diversity. In this paper, we
    propose a book context-aware recommender system (CARS) that
    uses individual characteristics as model features and active search
    as a pre-filtering context method in an attempt to increase user’s
    newness perception and diversity. To achieve this goal, we revised
    literary critic essays to create five binary base-questions able to
    separate and aggregate novels through subjective concepts.We also
    conducted a data collection to form a dataset around 50 selected
    books, evaluated by the public using these questions. Going further,
    we developed two recommender systems (RS) using different
    strategies to handle imbalanced samples (SELC and SMOTE) and
    compare their performance to conclude that SELC generates better
    recommendations on an inner performance.

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