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Paper Title : E-book recommendation system using content-based filtering

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.6585028

Authors: Aarush Gandhi, Akshat Patwal, Shaswat Kumar, Sushil Kumar, Shrankhla Saxena

         


Abstract
The system known as the book recommendation system could be a new form of web tool which helps users to get the names of the books of their interest. Using a web recommender is relatively an easy and quicker step to get names of the books and this can be done in very short time. This system directs our user towards those books which can meet their interest through cutting down large databases of books. Best strategy to increase profits and attract customers would be a recommendation system. The prevailing methodologies enable the systems to gather the immaterial information and cause a downfall in attracting the users and finishing there is a fast and reliable method. In this paper we provide the running model of the recommendation systems that's presently used in the web book searching domain. This research paper shows a simple system for book recommendations that help the user to get the best book of their interest. As we all know there are thousands of books of the same genre so all the readers are very confused about which book they should read first. For instance this book recommendation system comes into the picture which suggests you the best book of your interest so that you do not confuse further. In this research paper we showed you different models to get the best results of books for the customers. Models used are popularity based model (top in whole collection), Popularity based model (top in given place), Same author, and publisher of given book name, books popular yearly, average weighted rating based, correlation based. We also include some filtering techniques like -collaborative filtering, content filtering, nearest neighbor. Firstly we filter our data by using the filtering techniques and then we take intersections of all the models we stated above and then present our records to the customer.

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Keywords
- Collaborative filtering, Content based filtering, Memory based approach,Recommender system.