诺库维普

A General Model for Out-of-town Region Recommendation 2018-10-05 05:07:45

With the rapid growth of location-based social networks (LBSNs), it is now available to analyze and understand user mobility behavior in real world. Studies show that users usually visit nearby points of interest (POIs), located in small regions, especially when they travel out of their hometowns. ... || Location-based social networks; region recommendation; out- of-town recommendation; || Tuan-Anh Nguyen Pham...

What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation 2018-10-05 03:35:14

The rapid growth of Location-based Social Networks (LB- SNs) provides a vast amount of check-in data, which facili- tates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographi- cal influe... || POI as item, image as content of item, PMF, CNN, ; || Suhang Wang...

Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation 2018-10-05 00:35:10

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 recommenda- tion models based on conventional sequential-data modeling approaches have been proposed. How- ever, su... || Embedding model, Sequential information + content of POI ; || Buru Chang...

Context-aware Sequential Recommendation 2018-10-04 00:43:40

Since sequential information plays an importan- t role in modeling user behaviors, various sequential rec- ommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) b... || RNN理解; Sequential information add Contextual information; || Qiang Liu...

POI2Vec: Geographical Latent Representation for Predicting Future Visitors 2018-10-03 22:26:24

With the increasing popularity of location-aware social me- dia applications, Point-of-Interest (POI) recommendation has recently been extensively studied. However, most of the exist- ing studies explore from the users’ perspective, namely rec- ommending POIs for users. In contrast, we consider a ne... || Future Visitor Prediction, Sequential Recommendation, binary tree, Probability Estimation; || Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee...

Sequential Recommender System based on Hierarchical Attention Network 2018-10-03 22:17:17

With a large amount of user activity data accumu- lated, it is crucial to exploit user sequential behav- ior for sequential recommendations. Convention- ally, user general taste and recent demand are com- bined to promote recommendation performances. However, existing methods often neglect that use... || Sequential Recommendation, Long-term, Short-term, Dynamic, Next recommendation, Small, Gowalla, one day a session; || Haochao Ying; Hui Xiong...

Personalized Recommendation via Cross-Domain Triadic Factorization 2018-08-28 16:19:56

Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic... || Recommender System, Cross-Domain Collaborative Filtering, Triadic Factorization; 张量分解; || Liang Hu; Jian Cao...

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