194 citations · 424 across the 14 of their papers we have counts for
9 papers · 1 filter
Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Liang Yang +4
Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based…
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
Yu Tian, Jianxin Chang, Yannan Niu +2
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage…
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Cheng Zhao, Chenliang Li, Rong Xiao +2
In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interacti…
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Zhihong Chen, Rong Xiao, Chenliang Li +3
Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and…
A Capsule Network for Recommendation and Explaining What You Like and Dislike
Chenliang Li, Cong Quan, Li Peng +3
User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for…
A Review-Driven Neural Model for Sequential Recommendation
Chenliang Li, Xichuan Niu, Xiangyang Luo +2
Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting use…