With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user’s behavior amongst multiple domains and help them to find the desirabl... || Fashion recommendation; deep learning; cross-domain knowledge transfer; transfer learning; domain adaptation; CNN; || Shatha Jaradat...
Recommending routes for a group of competing taxi drivers is almost untouched in most route recommender systems. For this kind of problem, recommendation fairness and driving efficiency are two fundamental aspects. In the paper, we propose SCRAM, a sharing considered route assignment mechanism for f... || Recommender Systems; Assignment Mechanism; Fairness; Taxis; || Shiyou Qian; Jian Cao...
Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A... || Information retrieval; Learning to rank; Knowledge transfer; Ranking SVM; || Depin Chen,Yan Xiong, Jun Yan, Gui-Rong Xue, Gang Wang, Zheng Chen...
In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground- trut... || WWW; Diversity; Collaborative filtering; Recommender systems; Structural SVM; || Peizhe Cheng; Shuaiqiang Wang...
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques base... || Collaborative Filtering, Neural Networks, Deep Learning, Matrix Factorization, Implicit Feedback; NeuMF; || Xiangnan He; Hanwang Zhang...
