568 citations · 617 across the 16 of their papers we have counts for
16 papers
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
Sihao Hu, Yi Cao, Yu Gong +4
Short video has witnessed rapid growth in the past few years in e-commerce platforms like Taobao. To ensure the freshness of the content, platforms need to release a large number o…
GRN: Generative Rerank Network for Context-wise Recommendation
Yufei Feng, Binbin Hu, Yu Gong +3
Reranking is attracting incremental attention in the recommender systems, which rearranges the input ranking list into the final rank-ing list to better meet user demands. Most exi…
Revisit Recommender System in the Permutation Prospective
Yufei Feng, Yu Gong, Fei Sun +2
Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite cand…
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Runsheng Yu, Yu Gong, Xu He +4
A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…
Distant Supervision for E-commerce Query Segmentation via Attention Network
Zhao Li, Donghui Ding, Pengcheng Zou +6
The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supe…
Balanced Order Batching with Task-Oriented Graph Clustering
Lu Duan, Haoyuan Hu, Zili Wu +4
Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking pro…
EdgeRec: Recommender System on Edge in Mobile Taobao
Yu Gong, Ziwen Jiang, Yufei Feng +4
Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recomme…
Conceptualize and Infer User Needs in E-commerce
Xusheng Luo, Yonghua Yang, Kenny Q. Zhu +2
Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solution…
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
Yu Zhu, Yu Gong, Qingwen Liu +6
Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively interven…
Exact-K Recommendation via Maximal Clique Optimization
Yu Gong, Yu Zhu, Lu Duan +5
This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (const…
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
Jian-Guo Zhang, Pengcheng Zou, Zhao Li +4
Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe…
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Jianguo Zhang, Pengcheng Zou, Zhao Li +5
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over…
Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
Yunlun Yang, Yu Gong, Xi Chen
With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and effic…
Automatic Generation of Chinese Short Product Titles for Mobile Display
Yu Gong, Xusheng Luo, Kenny Q. Zhu +3
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a ne…
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Yu Gong, Xusheng Luo, Yu Zhu +6
Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such model…
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Jun Wang, Lantao Yu, Weinan Zhang +5
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a quer…