RBPR: A hybrid model for the new user cold start problem in recommender systems
RBPR: A hybrid model for the new user cold start problem in recommender systems
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DOI：
10.1016/j.knosys.2020.106732
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Junmei Feng;Zhaoqiang Xia;Xiaoyi Feng;Jinye Peng
中科院分区：
文献类型：
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作者：
Junmei Feng;Zhaoqiang Xia;Xiaoyi Feng;Jinye Peng
推荐系统旨在通过分析用户的偏好来预测用户的潜在需求，并提供个性化的推荐服务。用户偏好可以从显式或隐式反馈数据中推断出来。大多数现有的协同过滤（CF）方法严重依赖于显式反馈数据。然而，当评级数据稀疏时，这些方法的性能很差。在本文中，我们处理稀疏数据的极端情况，即新用户冷启动问题。为了克服这一问题，我们提出了一种新的CF排序模型，该模型将面向排序的概率矩阵分解（PMF）方法和面向成对排序的贝叶斯个性化排序（BPR）方法结合在一起。因此，我们提出的模型充分利用了显式和隐式反馈数据。基于4个公共数据集构建的新用户冷启动数据集的实验验证了该模型对冷启动推荐的有效性。所建议的方法的代码可在https://gitee.com/xia_zhaoqiang/recomender-systems-rbpr中获得。
The recommender systems aim to predict potential demands of users by analyzing their preferences and provide personalized recommendation services. User preferences can be inferred from explicit or implicit feedback data. Most existing collaborative filtering (CF) methods rely heavily on explicit feedback data. However, these methods perform poorly when rating data is sparse. In this paper, we deal with the extreme case of sparse data, i.e., the new user cold start problem. In order to overcome this problem, we propose a novel CF ranking model, which combines a rating-oriented approach of Probabilistic Matrix Factorization (PMF) and a pairwise ranking-oriented approach of Bayesian Personalized Ranking (BPR) together. Therefore, our proposed model makes full use of the explicit and implicit feedback data. Experiments on the constructed new user cold start datasets based on four public datasets demonstrate the effectiveness of the proposed model for cold start recommendation. Code for the proposed method is available in https://gitee.com/xia_zhaoqiang/recomender-systems-rbpr.