H
Haiqin Yang
Researcher at The Chinese University of Hong Kong
Publications - 83
Citations - 3468
Haiqin Yang is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Support vector machine & Question answering. The author has an hindex of 24, co-authored 81 publications receiving 3065 citations. Previous affiliations of Haiqin Yang include Hang Seng Management College.
Papers
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Proceedings Article
Fused matrix factorization with geographical and social influence in location-based social networks
TL;DR: This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) and fuse the geographical influence into a generalized matrix factorization framework.
Proceedings Article
Where you like to go next: successive point-of-interest recommendation
TL;DR: This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
Proceedings Article
Simple and Efficient Multiple Kernel Learning by Group Lasso
TL;DR: This paper forms a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL that leads to an efficient algorithm for MKL, but also generalizes to the case for Lp-MKL.
Proceedings Article
STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation
TL;DR: This paper proposes a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time and proposes a new interval-aware weight utility function to differentiate successive check-ins' correlations, which breaks the time interval constraint in prior work.
Book ChapterDOI
Support Vector Machine Regression for Volatile Stock Market Prediction
TL;DR: The experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index.