诺库维普

DAN : Deep Attention Neural Network for News Recommendation 2019-04-27 22:42:29

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, ... || CNN; RNN; Attention Model; 不一样的objective function； content-based; dynamic; 图画的不错；Adressa-1week; Adressa-10week; || Qiannan Zhu;...

Explainable Recommendation Through Attentive Multi-View Learning 2019-04-27 09:47:09

Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this p... || attention/attentive model； review information; explicit feature hierarchy (hierarchical); Microsoft Concept Graph; 雇人验证算法解释性好坏；算法鲁棒性robust验证（对用户分组验证结果不变）; || Jingyue Gao; Xing Xie...

Multi-order Attentive Ranking Model for Sequential Recommendation 2019-04-26 05:03:41

In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interac... || 物品和物品之间的关系通过weight表示出来（解释）；residual network ResNet; 讨论了temporary order在同一个session中的不重要性；attention model weight新求法； Yelp; Amazon; Movies&TV; CDs&Vinyl; || Lu Yu

HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-start Recommendation 2019-04-25 10:58:35

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item... || 通过gate neural network自动学习加权参加；为边建立vector representation；类似于OD pair一样将graph中的边也表示成vector；user-user; item-item; user-item; || Liang Hu...

What to Do Next: Modeling User Behaviors by Time-LSTM 2019-04-24 05:04:32

Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users’ actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional... || explicit time interval; time signal; LSTM; Phased LSTM; 融入时间信息； time interval2vec; || Yu Zhu...

Joint Representation Learning for Multi-Modal Transportation Recommendation 2019-04-23 22:59:39

Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation sys... || trans2vec; metric learning; graph; embedding; ; || Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong...

Collaborative Memory Network for Recommendation Systems 2019-04-22 06:00:10

Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learn- ing has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep ... || Augmented; Memory Network; attention model; || Travis Ebesu； Bin Shen； Yi Fang...

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