诺库维普

Dynamic Explainable Recommendation based on Neural Attentive Models 2019-04-22 05:57:21

Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dy... || CNN; GRU; Time-aware GRU； from static to dynamic explainable； attention mechinism; 权重可视化 visualization；可解释； interpret; Neural Attentive Model for Explainable Recommendation by Learning User Dynamic Preference; || Xu Chen； Yongfeng Zhang...

From Zero-Shot Learning to Cold-Start Recommendation 2019-04-13 05:50:35

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are inde- pendently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same ... || encoder; decoder; content-based; cold-start; Symmetric recovery; projection; lowe-rank; sparsity; || Jingjing Li...

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination 2019-04-13 05:47:51

Recent progress in deep learning is revolutionizing the health- care domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health his... || Memory Network; Graph model; RNN; EHR; DDI; longitudinal hidden state; 图模型结合Memory network模型; || Junyuan Shang...

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems 2019-04-13 05:38:16

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, cross-domain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or i... || content-based; review-based; score-based; SDAE; aSDAE; deep learning; cross-domain; || Wenjing Fu...

Non-Compensatory Psychological Models for Recommender Systems 2019-04-12 02:46:28

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multip... || Bradley-Terry Model; 偏好结构；decision procesison；非线性加权; || Chen Lin...

Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation 2019-04-12 00:35:00

Matrix Factorization (MF) is widely used in Recommender Systems (RSs) for estimating missing ratings in the rating matrix. MF faces major challenges of handling very sparse and large data. Poisson Factorization (PF) as an MF variant addresses these challenges with high efficiency by only computing o... || Poisson Factorization; content-based + CF; Gamma; graphical models; VI (Variational Inference)；属性集合; || Trong Dinh Thac Do...

Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation 2019-04-11 03:50:20

Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such m... || embedding learning; word2vec; content based; POI; skip-gram；初始化隐向量; || Buru Chang...

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