77 citations · 95 across the 4 of their papers we have counts for
6 papers · 1 filter
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
Tobias Schnabel, Mengting Wan, Longqi Yang
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving…
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
Zhiwei Liu, Mengting Wan, Stephen Guo +2
Within-basket recommendation reduces the exploration time of users, where the user's intention of the basket matters. The intent of a shopping basket can be retrieved from both use…
Addressing Marketing Bias in Product Recommendations
Mengting Wan, Jianmo Ni, Rishabh Misra +1
Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumer-product interactions. However, these interactions…
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
An Yan, Shuo Cheng, Wang-Cheng Kang +2
Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Mo…
Beyond "How may I help you?": Assisting Customer Service Agents with Proactive Responses
Mengting Wan, Xin Chen
We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems. Smart-reply systems have been widely applied in real-world applica…
Recommendation Through Mixtures of Heterogeneous Item Relationships
Wang-Cheng Kang, Mengting Wan, Julian McAuley
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data. Specific instances make use of signals ranging from user f…