关键词: 个性化推荐, 长尾物品, 评论挖掘, 情感分析, LSTM网络模型

Abstract: In the field of recommendation, many studies have considered the attribute dimension to improve the accuracy of the algorithm in terms of score-item, which is not enough to judge the user’s real attitude towards the product. In this paper, the LSTM neural network model is used to fuse the user comment emotion score and user score to calculate the user's real interest in the item. This paper uses vector space to classify user emotion into three categories, draws lessons from the idea of Gini coefficient in economics, introduces penalty factor, and gives play to the long tail ability of the system by adjusting popular items, so as to increase the coverage of recommendation results, and realize the balanced optimization of the recommended degree of popular items and unpopular items. Finally, experiments are carried out on the values of emotion coefficient and punishment coefficient parameters, and the optimal combination of model parameters is obtained. The results show that the accuracy and coverage of the model are more in line with user needs compared with other mainstream models. It is also proved that the commented emotion factors and punishment coefficient are effective for building a better recommendation model.

Key words: personalized recommendation, long tail items, comment mining, emotion analysis, LSTM network model