A Books Recommendation Approach Based on Online Bookstore Data

TL;DR

This paper proposes a book recommendation approach that combines factor analysis, fuzzy evaluation, and nearest neighbor prediction to improve personalized book suggestions based on online bookstore data.

Contribution

It introduces a novel multi-step method integrating factor analysis, fuzzy evaluation, and similarity-based prediction for book recommendation systems.

Findings

Factors affecting user preferences were identified and quantified.

The approach achieved accurate prediction of user evaluations.

The method enhances recommendation relevance in online bookstores.

Abstract

In the era of information explosion, facing complex information, it is difficult for users to choose the information of interest, and businesses also need detailed information on ways to let the ad stand out. By this time, it is recommended that a good way. We firstly by using random interviews, simulations, asking experts, summarizes methods outlined the main factors affecting the scores of books that users drew. In order to further illustrate the impact of these factors, we also by combining the AHP consistency test, then fuzzy evaluation method, empowered each factor, influencing factors and the degree of influence come. For the second question, predict user evaluation of the listed books from the predict annex. First, given the books Annex labels, user data extraction scorebooks and mathematical analysis of data obtained from SPSS user preferences and then use software to nearest neighbor analysis to result in predicted value.