诺库维普

WhyWe Go Where We Go: Profiling User Decisions on Choosing POIs 2019-11-14 10:44:06

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, wh... || scalar projection；一个利用scalar projection做的attention方法; || Renjun Hu; Xinjiang Lu...

Sequential Scenario-Specific Meta Learner for Online Recommendation 2019-11-07 20:13:24

Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our... || few-shot learning; meta learning; LSTM; early-stop policy; learning rate;; || Zhengxiao Du...

Session-based recommendations with recurrent neural networks 2019-11-05 20:02:13

We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). ... || GRU4Rec-basic; || Bal´azs Hidasi...

Streaming Session-based Recommendation 2019-11-05 09:23:30

Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of... || Session Recommendation; Streaming Recommendation; Attention Model; Matrix Factorization； GRU；paired sample t-test; 配对样本t检验; || Lei Guo...

Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks 2019-10-14 14:07:13

A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often cons... || purpose; session-based; ijcai; || Liang hu...

Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network 2019-09-27 16:51:31

Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the... || attention nn; ijcai；aggregation operation; || Zibin Zheng; Xiangnan He...

Hierarchical Gating Networks for Sequential Recommendation 2019-08-21 05:52:10

The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still... || 组织set（session）中的item embeddings; || Chen Ma...

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