Abstract
The goal of planned library recommender system is to provide needful library resources quickly. The important phases required to perform are build and update user profiles and search the proper library resources. This proposed system uses a hybrid approach for filtering available books of different subjects, research journal articles, and other resources. Content-based filtering evaluates user profile with available library resources. The results generated are satisfying the users need. The system can generate satisfactory recommendations, since in dataset most of the entries for books and research journal articles are rich with keywords. This richness is possible by referring to abstract and TOC (table of contents) while adding records of research journals articles and books, respectively. Collaborative filtering computes recommendations by searching users with similar interests. Finally, to the active user, recommendations are provided which are generated with the hybrid approach. To make it simpler and develop the outcome of the recommendation process, categorization of available records is made into distinct classes. The distinct classes are defined in ACM CCS 2012. The classifier is the output of relevant machine learning methods. The paper discusses the improvement in results by hybrid approach due to the use of classified library resources.
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Shirude, S.B., Kolhe, S.R. (2020). Improved Hybrid Approach of Filtering Using Classified Library Resources in Recommender System. In: Mandal, J., Sinha, D. (eds) Intelligent Computing Paradigm: Recent Trends. Studies in Computational Intelligence, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-13-7334-3_1
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DOI: https://doi.org/10.1007/978-981-13-7334-3_1
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