Probabilistic Recommendation Model Integrating Personality Features

doi:10.3778/j.issn.1673-9418.2106111

Abstract

Abstract: The model-based collaborative filtering algorithms need to analyze and extract the basic feature matrix of “user items” for user recommendation. Different user characteristics directly lead to different user first foundations. However, collaborative filtering algorithms for model purposes only consider the analysis and extraction of key factors that affect item characteristics, but not the important factors that affect user characteristics. This type of traditional model often initializes the user’s latent feature vector randomly and assigns an assumed normal distribution, resulting in no data changes in these models that can have a direct impact on the results of the user’s potential feature modeling. In addition, the user-based recommendation system model directly uses the user’s com-ments and ratings as user characteristics. The reference methods of these data in the traditional recommendation system and the data itself are not enough to support the acquisition of the essential characteristics of users. The approximation of these features also cannot meet the needs of personalized recommendation. Aiming at the problems of existing recommendation algorithms that there is no analysis to extract the essential features of users and the insufficient extraction of essential features of items, and the recommendation results are difficult to reflect the user’s personality, a recommendation model that integrates personality features is proposed. Firstly, according to the non-structure of users in the recommendation platform, the review text information is transformed, the persona-lity characteristics are used as the direct influencing factors of the user characteristics, a neural network model is designed to calculate the BIG FIVE personality score of the review user, and the personality score is vectorized as the user characteristics; then the item characteristics are obtained through the project review text information. This paper designs a collaborative learning framework for personality perception, and defines a loss function to obtain the feature vectors of users and items. Finally, target users are recommended based on the results of user and item characterization. A comprehensive experimental verification is carried out on 3 datasets. The results show that the algorithm outperforms the comparison algorithms in terms of prediction accuracy, F1 value, AUC index, etc. Through personality modeling, it can recommend items that are more in line with users?? preferences.

Key words: personality recognition, semantic analysis, recommended system, collaborative learning, probabilistic matrix decomposition

摘要： 以模型为基础的协同过滤推荐算法需分析提取“用户-项目”特征矩阵以进行用户推荐。用户特征不同直接导致用户偏好不同，但以模型为基础的协同过滤推荐算法仅考虑分析提取影响项目特征的关键因素而未考虑分析提取影响用户特征的重要因素，这类传统模型往往将用户潜在特征向量随机初始化，并赋予一个假定的正态分布，导致这些推荐系统模型中没有任何一项数据变化可以对用户潜在特征建模结果产生直接影响。另外基于用户的推荐系统模型往往将用户的评论、评分的信息直接近似作为用户特征，传统推荐系统中这些数据引用方式和这些数据本身不足以支撑获取用户的本质特征。这些特征的近似也不能满足个性化推荐的需求。针对现有推荐算法所面临的没有分析提取用户本质特征以及项目本质特征提取不充分进而推荐结果难以体现用户个性的问题，提出一种融合人格特征的推荐模型。首先根据推荐平台中用户的非结构化评论文本信息，将人格特征作为用户特征的直接影响因素，设计一个神经网络模型计算评论用户的BIG FIVE人格得分，并将人格得分向量化作为用户特征；然后通过项目的评论文本信息获得项目特征；设计人格感知的协同学习框架，定义损失函数获取用户、项目的特征向量；最后根据用户、项目表征结果对目标用户进行推荐。在3个数据集上进行了全面的实验验证，结果表明算法在预测准确率、F1值、AUC指标等方面表现优于对比算法，通过人格建模，能够为用户推荐更符合其偏好的项目。

关键词: 人格识别, 语义分析, 推荐系统, 协同学习, 概率矩阵分解

SHEN Tiesunlong, FU Xiaodong, YUE Kun, LIU Li, LIU Lijun. Probabilistic Recommendation Model Integrating Personality Features[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 251-262.

沈铁孙龙, 付晓东, 岳昆, 刘骊, 刘利军. 融合人格特征的概率推荐模型[J]. 计算机科学与探索, 2023, 17(1): 251-262.

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