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Research on Personalized Book Recommendation Based on Improved Similarity Calculation and Data Filling Collaborative

Yanping Du¹, Lizhi Peng¹, Shuihai Dou¹

¹Beijing Institute of Graphic Communication, Beijing Key Laboratory of Digitalized Printing Equipment, Beijing, China.

Computational Intelligence and Neuroscience

|September 27, 2022

Summary

This study enhances book recommendations by improving collaborative filtering algorithms to address data sparsity and cold start issues. The new methods significantly boost recommendation accuracy and quality.

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Area of Science:

Computer Science
Information Science

Background:

Traditional collaborative filtering struggles with data sparseness and cold start in personalized recommendations.
Inaccurate recommendations reduce user satisfaction and platform effectiveness.

Purpose of the Study:

To propose an improved collaborative filtering algorithm for book recommendations.
To enhance similarity calculation and data imputation methods to overcome data limitations.

Main Methods:

Introduced user common rating weight and average book rating threshold for similarity calculation.
Implemented hierarchical clustering based on user attributes and Euclidean distance for user grouping.
Utilized the Shope-one algorithm with weighted degree for improved missing data imputation.

Main Results:

Experimental validation using the Book-Crossing dataset in Python.
Demonstrated significant improvements in recommendation accuracy.
Showcased enhanced recommendation quality compared to traditional methods.

Conclusions:

The proposed improved collaborative filtering algorithm effectively addresses data sparseness and cold start.
Enhanced similarity and imputation methods lead to superior personalized book recommendations.