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...
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...
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...
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...
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...
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...
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...
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...
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...
This paper presents some of the most common Chinese-English habits observed from over two hundred English technical papers by Chinese writers. The habits are explained and in most cases, example text from an actual paper is given along with preferred text. An attempt is made to explain how to correc... || 写作指导；老外总结的中国人写英语文章最常犯的错误总结; || Felicia Brittman...
