Abstract
Many recommendation algorithms have been developed using collaborative filtering such as latent matrix factorization. But most of these algorithms are based on other similar users rating of the books and have certain limitations. Reviews given to books provide a valuable source of information for recommender systems. Algorithms used in area of natural language processing are based on similarity between word vectors in the two texts or documents. Often context between these words are missing leading to poor recommendation as it requires semantics to be analyzed between texts from the given reviews. Deep learning has been widely used in natural language processing and has obtained a great progress in this field. In this paper, we use Recurrent Neural Networks (RNN) as a deep learning approach for recommending books. RNN is an improvement over the existing models as instead of each input unit being independent as in logistic regression and other neural networks each neuron can use its internal memory to maintain information about the previous units. Hence, the context between reviews is maintained by memory units and hence a more accurate classification can be made that leads to better recommendation.
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Saraswat, M., Saraswat, R., Bahuguna, R. (2022). Recommending Books Using RNN. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_7
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DOI: https://doi.org/10.1007/978-981-16-8892-8_7
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