167 citations · 526 across the 20 of their papers we have counts for
6 papers · 1 filter
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
Jun Zhao, Zhou Zhou, Ziyu Guan +4
The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences)…
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
Wei Zhao, Boxuan Zhang, Beidou Wang +6
We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of prod…
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…
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Yu Zhu, Jinhao Lin, Shibi He +4
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-star…
A Brand-level Ranking System with the Customized Attention-GRU Model
Yu Zhu, Junxiong Zhu, Jie Hou +4
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance…