打包推荐算法综述

doi:10.3969/j.issn.1006-2475.2025.09.001

摘要/Abstract

摘要：
摘要：打包推荐通过组合多个相关联的商品、服务或内容，并从中优化出最优解进行推荐，能够满足用户的多方面需求。随着电子商务、旅游零售等领域的快速发展，打包推荐已成为提升用户体验和商业效益的重要手段。本文综述打包推荐算法的研究进展与应用现状。首先，明确任务定义、任务特性、任务挑战以及常用评测指标，其中任务挑战包括捆绑包整体性问题、捆绑包多样性问题、数据稀疏问题、冷启动问题以及捆绑包生成问题等。然后，将现有算法划分为基于数据挖掘的方法、基于传统机器学习的方法和基于深度学习的方法3个大类和7个子类，并深入分析各类方法的特点。接着，总结打包推荐领域常用的评测数据集。最后，对打包推荐算法的未来发展趋势进行展望。

Abstract:
Abstract： Bundle recommendation refers to optimizing and recommending the best solution by combining multiple related goods， services， or content， which can meet the various needs of users. With the rapid development of sectors like e-commerce and travel retail， bundle recommendation has become an important approach to improve user experience and business benefits. This paper reviews the research progress and application status of bundle recommendation algorithms. Firstly， the task definition， task characteristics， task challenges， and commonly used evaluation metrics are clarified. The task challenges include the integrity of bundled packages， diversity of bundled packages， data sparsity， cold start problems， and bundle generation problems. Secondly， the existing algorithms are classified into three major categories， data mining-based algorithms， traditional machine learning-based algorithms， deep learning-based algorithms， and further sorted out into seven subcategories. The characteristics of each category are thoroughly analyzed. Thirdly， commonly used datasets for the bundle recommendation task are summarized. Finally， the future development trends of bundle recommendation are discussed.

Key words: Key words： bundle recommendation, combinatorial optimization, data mining, deep learning, machine learning

中图分类号:

TP391

李雄清1, 2, 彭明田1, 2, 李永1, 2, 王骏飞1, 2, 刘德志1, 3, 卞宇轩1, 3, 柴阅林1, 3, 刘云韬1, 3. 打包推荐算法综述[J]. 计算机与现代化, 2025, 0(09): 1-13.

LI Xiongqing1, 2, PENG Mingtian1, 2, LI Yong1, 2, WANG Junfei1, 2, LIU Dezhi1, 3, BIAN Yuxuan1, 3, CHAI Yuelin1, 3, LIU Yuntao1, 3. Survey on Bundle Recommendation Algorithms[J]. Computer and Modernization, 2025, 0(09): 1-13.

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