Wang Jun, Hu Rongjie
School of Mathematics & Statistic, Changchun University of Technology, Changchun, China.
Heliyon. 2024 Sep 21;10(19):e38195. doi: 10.1016/j.heliyon.2024.e38195. eCollection 2024 Oct 15.
Financial product recommendation algorithms are mainly product-centered. This article proposes a two-stage recommendation optimization algorithm based on item popularity and user features, named CPCF-TSP, that can make full use of the demographic characteristics of users and mitigate the problem of users being more inclined to choose "hot" financial products. A popularity weight factor is introduced to normalize popularity and modify Pearson's similarity function. The modified Pearson's similarity function is combined with popularity normalization and user features to improve modeling performance. The two-stage recommendation optimization procedure was combined with a collaborative filtering algorithm to improve recommendation precision. CPCF-TSP fully considers user features in building a hybrid recommendation model and solves the problem of user cold-start. It can also mitigate popularity deviations and improve recommendation precision. MovieLens data and Santander Bank client trading data were used in a case study. The results show that the algorithm reduces inaccuracy in the calculation of the weights for recommendation popularity and similarity and is especially suitable for recommending financial products in which user information can be easily collected and the number of users is far greater than the number of products considered.
金融产品推荐算法主要以产品为中心。本文提出了一种基于项目流行度和用户特征的两阶段推荐优化算法，名为CPCF-TSP，该算法可以充分利用用户的人口统计学特征，并缓解用户更倾向于选择“热门”金融产品的问题。引入了一个流行度权重因子来对流行度进行归一化，并修改皮尔逊相似性函数。将修改后的皮尔逊相似性函数与流行度归一化和用户特征相结合，以提高建模性能。两阶段推荐优化过程与协同过滤算法相结合，以提高推荐精度。CPCF-TSP在构建混合推荐模型时充分考虑了用户特征，解决了用户冷启动问题。它还可以减轻流行度偏差并提高推荐精度。在案例研究中使用了MovieLens数据和桑坦德银行客户交易数据。结果表明，该算法减少了推荐流行度和相似性权重计算中的不准确性，特别适用于推荐用户信息易于收集且用户数量远大于所考虑产品数量的金融产品。
