Machine Learning based Efficient Recommendation System for Book Selection using User based Collaborative Filtering Algorithm

Kommineni, Madhuri; Pyla, Alekhya; Vyshnavi, T. Mohana; Aparna, V.; Swetha, K.; Mounika, V.

doi:10.1109/icisc47916.2020.9171222

Cited by 31 publications

(8 citation statements)

References 6 publications

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“…Customers can find and purchase the book they want to read next by using a book recommendation system that uses user-based collaborative filtering, machine algorithm knn , user ratings, and similar interests from other customers [5].The book recommendation system employs collaborative filtering and suggests hybrid algorithms, one of which is Eclat, which is quicker and more effective than the others. The books that have the greatest user ratings and are of the highest quality will be suggested for purchase to those who express interest in them [6].…”

Section: Literature Surveymentioning

confidence: 99%

Book Recommendation System with Integrated Chatbot

Pawar

2024

IJSREM

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In today’s digital era, the overwhelming volume of available literature necessitates a more personalized approach to book recommendations. Our research aims to address this need by pioneering a solution that integrates hybrid filtering methodologies and advanced chatbot technology driven by machine learning algorithms. By leveraging these cutting-edge techniques, we aspire to redefine the book discovery experience, providing tailored recommendations that resonate with individual preferences. Through the fusion of popularized, collaborative and content-based alongside the capabilities of machine learning models for natural language processing, our system not only enhances recommendation accuracy but also fosters real-time interaction, ultimately enhancing user satisfaction and engagement in the realm of literature. Key Words- Personalized book recommendations, Hybrid filtering methodologies, chatbot technology, machine learning algorithms, Collaborative filtering, Content-based filtering, Natural language processing, Recommendation accuracy.

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Section: Literature Surveymentioning

confidence: 99%

Book Recommendation System with Integrated Chatbot

Pawar

2024

IJSREM

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“…User-based means the similarity between users while item-based refers to the similarity between the similar users rated items. User-based collaborative filtering (UBCF) was used by [13] in their recommendation system to recommend books to a user based on similarity measures. The dataset is 'Goodreads-books', which was obtained from Kaggle.…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

Analysis of Book Preferences Among Visitors in Library System

Foo,

Ng,

Haw

et al. 2024

TEM Journal

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Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.

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“…We have used a User-Based Collaborative Filtering approach and measured the performance of similarity measures in recommending books to a user. The proposed system's overall architecture is introduced and its implementation is represented with a model design [7]. Recently, virtual assistants for customer service have become more and more popular with customer-oriented businesses.…”

Section: Related Workmentioning

confidence: 99%

Virtual Auction featured with AI Chat System & Payment Provision (VACP)

Railkar¹

2022

IJSREM

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The design and the implementation of an effective virtual auction service providing a platform for clients and vendors is presented in this paper. It uses a very efficient way of purchasing and selling any products through a virtual auction system. Virtual auctions reach buyers and sellers worldwide. Virtual auctions provide the reach to find the right buyer for every item. By using this system any person or an organization such as a government, or business institution can be more modular and organized in their buying and selling. Features like an AI chat system, Payment provision make this system more attractive and efficient. The system has been designed in such a manner that the users of the system will get all the necessary information, and it is capable of providing an easy, effective and reliable way of purchasing and selling via a virtual auction system which will change the usual scenario of auctions which happens in the real world. Key Words: machine learning, e-commerce, web application, payment gateway, assistive technology, virtual auction.

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Machine Learning based Efficient Recommendation System for Book Selection using User based Collaborative Filtering Algorithm

Cited by 31 publications

References 6 publications

Book Recommendation System with Integrated Chatbot

Book Recommendation System with Integrated Chatbot

Analysis of Book Preferences Among Visitors in Library System

Virtual Auction featured with AI Chat System & Payment Provision (VACP)

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