Techniques for cross-domain recommendation:A survey
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摘要: 随着信息技术和互联网的飞速发展，信息过载的问题日趋严重.个性化推荐系统是解决这一问题的热门技术.推荐系统的核心在于推荐算法，在过去的十年里，基于单领域的协同过滤推荐算法应用最为广泛.但用户和项目数量的急剧增长使得传统的协同过滤推荐算法面临冷启动和数据稀疏问题的挑战.跨领域推荐旨在整合来自不同领域的用户偏好特征，针对每个用户自身特点进行智能化感知，精准满足用户个性化需求，从而提高目标领域推荐结果的准确性和多样性，现已成为推荐系统研究领域中的热门话题.本文首先对跨领域推荐技术进行系统地研究和分析，概述跨领域推荐算法的相关概念、技术难点；其次对现有的跨领域推荐技术进行分类，总结出各自的优点及不足；最后对跨领域推荐算法的性能分析方法进行详尽的介绍.Abstract: With the rapid development of information technology and Internet, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Personalized recommendation system is a popular technology to deal with the information overload and recommendation algorithms are the core of it. In the past decades, collaborative filtering recommendation algorithm based on single domain has been widely used in many applications. However, the problems of cold start and data sparsity usually result in overfitting and fail to give desirable performance. The cross-domain recommendation techniques have been a hot topic in the field of recommender systems, which aim to utilize knowledge from related domains to perform or improve recommendation in the target domain. This paper carries out a systematic study and analysis of cross-domain recommendation techniques. First, we summarize the related concepts and the technical difficulties of cross-domain recommendation algorithms. Second, we present a general categorization of cross-domain recommendation techniques and sum up their respective advantages and disadvantages. Finally we introduce the method of performance analysis of cross-domain recommendation algorithm in detail.
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图 4 不规则的用户-项目-领域三阶张量转换为规则的张量
Fig. 4 Slices of rating matrices for each domain are transformed into a cubical tensor
表 1 跨域推荐各模型的优点和缺点
Tab. 1 Advantages and disadvantages of different methods in cross-domain recommendation
跨域推荐场景 方法 优点 缺点 领域间用户
完全重叠基于协同过滤
关系的方法基于矩阵合并的
跨域推荐[10-11]简单, 能直接应用传统
的协同过滤推荐算法不同领域需要一致的
评分机制, 忽略领域
差异性基于联合矩阵分解的
跨域推荐[12-14]框架灵活, 效果显著 对权重参数敏感, 导致
难以完全保留领域间
的差异性基于张量分解的
跨域推荐[15-17, 21]保留领域独立性特征,
对解决冷启动问题
效果显著必须构建出规则的
多阶张量基于语义关系的方法 基于图模型的跨域
推荐[23-24]能够解决数据异构、数
据稀疏、冷启动问题需要学习大量的参
数, 训练时间长基于深度学习的方法 / / / 领域间用户
完全不重叠基于协同过滤关系
的方法基于联合矩阵分解的
跨域推荐[28-29]框架灵活, 效果显著 对用户、项目之间相
似度的计算敏感基于评分聚类的跨域
推荐[1, 6, 8, 32]模型简单, 易于训练 缺少理论支撑, 领域
间必须存在强相关基于语义关系的方法 基于标签分类体系的
跨域推荐[36-38]利用外部知识库最大
程度上找出领域间偏
好相似的用户对标签信息和外部
知识库要求高基于语义关系图的跨
域推荐[41-44]很好的解决数据异构
和数据稀疏问题对外部知识库敏感,
训练复杂基于LDA主题模型的
跨域推荐[40]在目标领域没有用户
行为数据时也能有较
好的推荐性能对标签信息稀疏程度
敏感, 影响模型准
确度基于深度学习的方法 / / / 领域间部分
重叠基于协同过滤关系的
方法基于用户近邻的跨域
推荐[11, 46-47]模型简单, 能够提供
推荐解释对领域间用户重叠
程度敏感基于联合矩阵分解的
跨域推荐[45]框架灵活, 效果显著 基于图模型的跨域
推荐[48-49]能够解决数据异构、数
据稀疏、冷启动问题需要学习大量的参
数, 训练时间长基于语义关系的方法 / / / 基于深度学习的方法 基于Embedding技术
的跨域推荐[50]基于已有word2vec工
具, 训练过程相对
简单、稳定处于初步研究阶段,
还有很多需要进一步
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表 2 跨领域推荐算法性能评测指标
Tab. 2 Summary of metrics used for the evaluation of cross-domain recommendation
类别 度量指标 相关文献 实验方法 准确度 预测的精度指标 MAE [1][4][8][9][10][11][12][13][15]
[17][12][23][28][32][33][34][48]离线实验、在线实验 RMSE [4][13][12][23][27][45] 分类的精度指标 Precision [23][24][31][40][43][44] Recall [17][23][24][31][40][44] 排序的精度指标 MAP [12][23][45] AUC [14][17] F1-Measure [10][23][40] nDCG [24] 覆盖率 信息熵、基尼系数 / 多样性 推荐列表中项目两两之间的不相似性 / 用户体验 新颖度、惊喜度、用户满意度 / 在线实验、用户调查
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