General recommender and sequential recommender are two applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors, whereas sequential recommender focuses on exploring the item-to-item s... || multi-task learning ; sequential (item-to-item relations); || Disheng Dong...
Most existing recommender systems leverage theprimary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed asImplicit Recommender Systems). We propose to model the pairwise rankin... ; || Jingtao Ding...
Entity recommendation, providing search users with an improved experience by assisting themin finding related entities for a given query, has become an indispensable feature of today’s Websearch engine. Existing studies typically only consider the query issued at the current time step while igno... || Entity recommendation; Baidu； BiLSTM; || Huang, Jizhou and Zhang, Wei and Sun, Yaming and Wang, Haifeng and Liu, Ting...
Neural-based multi-task learning has been successfully used in many real-world large-scale applications such as recommendation systems. For example, in movie recommendations, beyond providing users movies which they tend to purchase and watch, the system might also optimize for users liking the movi... || KDD 2018; multi-task learning; mixture of experts; neural network; recommendation system； Shared-Bottom model; || Jiaqi Ma...
In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user’s historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories t... || Next item; metric learning； long- and short-term; time signal; 时间信号; || Shuai Zhang...
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, curren... || session-based; diversity; wide-in-wide-out; Tmall; sequential; || Liang Hu...
Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits from Electronic Health Records (EHR) has broad applications in healthcare analytics. Patient EHR data consists of a sequence of visits over time, where each visit includes multiple medical concepts... || 2Vec; Representation Learning; Medical Concepts; Healthcare An- alytics; Neural Networks; || Edward Choi...
User preferences are usually dynamic in real-world recommender systems, and a user’s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms – including both shallow and deep approaches – usually embed a user’s historical ... || Sequential Recommendation; Memory Networks; Collaborative Filtering; || Xu Chen...
