Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Q1 Decision Sciences

Annals of Data Science Pub Date : 2023-06-08 DOI:10.1007/s40745-023-00474-4

Clemens Tegetmeier, Arne Johannssen, Nataliya Chukhrova

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引用次数: 0

Abstract

Book recommender systems provide personalized recommendations of books to users based on their previous searches or purchases. As online trading of books has become increasingly important in recent years, artificial intelligence (AI) algorithms are needed to recommend suitable books to users and encourage them to make purchasing decisions in the short and the long run. In this paper, we consider AI algorithms for so called collaborative book recommender systems, especially the matrix factorization algorithm using the stochastic gradient descent method and the book-based k-nearest-neighbor algorithm. We perform a comprehensive case study based on the Book-Crossing benchmark data set, and implement various variants of both AI algorithms to predict unknown book ratings and to recommend books to individual users based on the highest predicted ratings. This study aims to evaluate the quality of the implemented methods in recommending books by using selected evaluation metrics for AI algorithms.

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协同图书推荐系统的人工智能算法

图书推荐系统根据用户以往的搜索或购买情况，向用户提供个性化的图书推荐。近年来，图书在线交易变得越来越重要，因此需要人工智能（AI）算法向用户推荐合适的图书，并鼓励他们在短期和长期内做出购买决定。在本文中，我们考虑了适用于所谓协作式图书推荐系统的人工智能算法，特别是使用随机梯度下降法的矩阵因式分解算法和基于图书的 k-nearest-neighbor 算法。我们基于 Book-Crossing 基准数据集进行了全面的案例研究，并实施了这两种人工智能算法的各种变体，以预测未知图书评分，并根据最高预测评分向单个用户推荐图书。本研究旨在使用选定的人工智能算法评价指标，评估所实施方法在推荐图书方面的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.