诺库维普

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts 2019-01-28 22:31:54

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...

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors 2019-01-28 22:28:02

Next-item Recommendation, Sequential Behaviors, Item Embed- ding, Recurrent Neural Networks; || Zhi Li...

Next Item Recommendation with Self-Attentive Metric Learning 2019-01-23 06:17:17

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...

Diversifying Personalized Recommendation with User-session Context 2018-12-19 01:05:33

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...

Sequential Recommendation with User Memory Networks 2018-10-11 02:45:26

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...

Key-Value Memory Networks for Directly Reading Documents 2018-10-10 10:57:06

Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restric... || Key-Value Memory Model, End-to-End, softmax; || Alexander H. Miller...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022