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1,122 results on '"Guo Mingming"'

1. Relationship Between Herbaceous Plant Diversity and Soil Factors at a Gully Head After Revegetation in Baicao Tableland

Author

Chen Zhuoxin, Guo Mingming, Wang Wenlong, Feng Lanqian, Lou Yibao, Zhu Yanan, and Yang Hao

Subjects
gully head, herbaceous plant diversity, soil property, vegetation restoration, baicao tableland, loess plateau, Environmental sciences, GE1-350, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

[Objective] The changes in herbaceous plant diversity and the factors influencing those changes after revegetation at a gully head were studied in order to provide a relevant foundation for the assessment of the benefits of soil and water conservation in the loess tableland. [Methods] Herbaceous plant diversity at a gully head was measured and soil property indexes related to the topsoil (0—10 cm and 10—25 cm) were determined by selecting grassland gully heads with vegetation restoration periods of 5, 13, 18, and 22 years in the Baicao Tableland. Farmland was used as the control. [Results] The Patrick richness index initially increased with increasing number of vegetation restoration years, and then stabilized. The Shannon-Wiener index showed a continuously increasing trend, and the Shannon-Wiener index values in 18 and 22 years of vegetation restoration were greater than at 13 years of restoration. However, the differences were not significantly different. The Pielou evenness index exhibited a fluctuating trend of increasing, and then decreasing, then increasing again, reaching a maximum value (0.89) in 22 years of recovery. The soil bulk density in both the 0—10 cm and 10—25 cm layers reached a minimum value in 22 years of recovery, with significant decreases of 13.1% and 17.8%, respectively, compared with values observed for farmland (p

Published
2023
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2. Genomic and transcriptomic analysis of breast cancer identifies novel signatures associated with response to neoadjuvant chemotherapy

Author

Yin, Gengshen, Liu, Liyuan, Yu, Ting, Yu, Lixiang, Feng, Man, Zhou, Chengjun, Wang, Xiaoying, Teng, Guoxin, Ma, Zhongbing, Zhou, Wenzhong, Ye, Chunmiao, Zhang, Jialin, Ji, Changhua, Zhao, Linfeng, Zhou, Peng, Guo, Yaxun, Meng, Xingchen, Fu, Qinye, Zhang, Qiang, Li, Liang, Zhou, Fei, Zheng, Chao, Xiang, Yujuan, Guo, Mingming, Wang, Yongjiu, Wang, Fei, Huang, Shuya, and Yu, Zhigang

Published
2024
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31. Online Product Feature Recommendations with Interpretable Machine Learning

Author

Guo, Mingming, Yan, Nian, Cui, Xiquan, Hughes, Simon, and Jadda, Khalifeh Al

Subjects
Computer Science - Information Retrieval
Abstract

Product feature recommendations are critical for online customers to purchase the right products based on the right features. For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices. However, determining the set of product features that most differentiate a particular product is still an open question in online recommender systems. In this paper, we focus on using interpretable machine learning methods to tackle this problem. First, we identify this unique product feature recommendation problem from a business perspective on a major US e-commerce site. Second, we formulate the problem into a price-driven supervised learning problem to discover the product features that could best explain the price of a product in a given product category. We build machine learning models with a model-agnostic method Shapley Values to understand the importance of each feature, rank and recommend the most essential features. Third, we leverage human experts to evaluate its relevancy. The results show that our method is superior to a strong baseline method based on customer behavior and significantly boosts the coverage by 45%. Finally, our proposed method shows comparable conversion rate against the baseline in online A/B tests.

Published
2021

32. Deep Learning-based Online Alternative Product Recommendations at Scale

Author

Guo, Mingming, Yan, Nian, Cui, Xiquan, Wu, San He, Ahsan, Unaiza, West, Rebecca, and Jadda, Khalifeh Al

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customer needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12 percent in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

Published
2021

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