Fu Junchen, Qi Zhaohui
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, New Territories, 999077 HKSAR People's Republic of China.
College of Information Science and Engineering, Hunan Normal University, Changsha, 410081 Hunan People's Republic of China.
J Supercomput. 2022;78(16):17776-17796. doi: 10.1007/s11227-022-04580-7. Epub 2022 May 23.
E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.
电子商务平台通常会训练其推荐系统模型，以根据用户行为数据实现个性化推荐。用户行为可分为隐式反馈和显式反馈。显式反馈数据已得到充分研究。然而，隐式反馈数据仍存在许多问题，例如行为数据类型多样、缺乏负面反馈以及缺乏表达真实用户偏好的能力。针对隐式反馈的这些问题，我们提出了一种基于潜在因子模型的TDF-WNSP-WLFM（时间衰减因子-负样本可能性权重-加权潜在因子模型）用于产品推荐。我们的方法主要专注于重构隐式评分矩阵，以使算法表现得更好。TDF-WNSP-WLFM算法在来自淘宝和REES46这两个大型电子商务平台的两个公共用户行为数据集上进行了测试。我们的算法与其他已知的协同过滤方法相比具有优势。
