Enhancing Personalized Book Recommender System

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using...

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Bibliographic Details
Published in:International journal of advanced networking and applications 2022-11, Vol.14 (3), p.5486-5492
Main Authors: Usman, Abdulgafar, Roko, Abubakar, B. Muhammad, Aminu, Almu, Abba
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Benchmarks
Bookstores
Collaboration
Content analysis
Datasets
Documents
Error reduction
Euclidean geometry
Filtering systems
Filtration
Libraries
Performance measurement
Recommender systems
Root-mean-square errors
Similarity
Similarity measures
User needs
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Summary:Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computes document similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. The performance of the proposed scheme was evaluated against the benchmark scheme using different performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.
ISSN:0975-0290
0975-0282
DOI:10.35444/IJANA.2022.14311