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Alternative TitleResearch and application of improved ensemble learning algorithm in e-commerce recommendation
Thesis Advisor石刚
Keyword集成学习 推荐系统 梯度提升决策树 特征工程
Call NumberTP181/S96/2018
Degree Discipline检测技术与自动化装置
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳


Other Abstract

As the largest e-commerce company in China and in the world, Alibaba opened a batch of access data of the real user behavior of Tmall mall in 2016. It hopes to find the user's hobby by analyzing the desensitization log of 4 months in the consumer's history, and then recommends its preferences for the user. This paper studies how to realize personalized e-commerce recommendation through the user's historical behavior and the property of the goods under the lack of understanding of the specific information of users and goods. In view of the fact that the existing user behavior data are too sparse and lack of objective conditions such as user ratings, collaborative filtering and other classical algorithms are not effective. This paper deals with data from data analysis, data cleaning, multi-dimensional Feature Engineering and feature selection, and puts forward an integrated learning method based on gradient lifting decision tree for the above problems, which transforms the recommendation problem into a problem that predicts the availability of the purchase of goods by the user. In order to solve the problem that GBDT is easy to fit and train slowly, this paper proposes a solution and makes three main tasks in it. (1) a regularization method based on model parameters is proposed to reduce the degree of overfitting of boosting. (2) an optimization method based on Newton method is proposed. This method is used to replace the existing gradient descent optimization method. Through experiments, it is proved that the method can converge to the optimal solution faster. (3) an approximate algorithm based on the loci is proposed, which is used to replace the present exact algorithm. The experiment shows that the method approximated the exact algorithm in the accuracy rate. Compared with the original algorithm, a certain amount of time cost is reduced. Finally, the experimental results show that the improved GBDT implementation is better than the traditional collaborative filtering method and some classical integrated learning algorithms. The research results of this paper provide new ideas and effective methods for the collaborative filtering algorithm and the GBDT single model in the complex data, which has low efficiency and lack of recommendation effect, which makes the recommendation effect a great improvement.

Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
孙靖哲. 改进集成学习算法在电商推荐中的研究与应用[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.
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