Designing a Book Recommender Engine: A New Perspective with Deep Learning Techniques

Radha Guha

Abstract


From the last decade, deep learning technology is showing amazing performance improvement in the field of computer vision and natural language processing (NLP). NLP’s big leap forward recently has enabled computers to understand ambiguous human languages decently. In this paper benefit of deep learning techniques in Book Recommender system design is explored and validated. As every book is huge in content, content-based filtering used for recommendation system design can benefit from NLP’s breakthrough word embedding technique which captures word context, semantics, and word dependency better and helps in dimensionality reduction as well. Subsequent advancement in language model with attention-based transformer architecture deciphers word and sentence meaning better considering a larger context. Content based filtering computes nearest neighbor recommendation and this technique will benefit as cosine similarity of one book to another can be computed more efficiently now. A second method used for recommender design is collaborative filtering which analyzes users’ past item preference, and user to user and item to item similarity computation. Deep learning techniques captures non-trivial, non-linear, user-item interaction better than traditional matrix factorization algorithms. Deep learning trains its model with huge amount of data in its parallel processing architecture. Multi-core CPU, GPU and TPU will support deep learning’s parallel processing architecture to handle bigdata to capture complex user-item interaction hierarchy. The contribution of this paper is to explain recommendation system design aspects, deep learning technology and comparison of deep learning with traditional machine learning techniques by solving a book recommendation system design.


Keywords


E-commerce, Recommendation System, Bookcrossing dataset, Project Gutenberg’s e-books, Content based filtering, Cosine distance, Collaborative filtering, Matrix Factorization, Deep learning, Auto Encoder, RMSE, MAE, Hit rate



DOI: http://doi.org/10.11591/ijict.v12i2.pp%25p

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