Title: Deep Learning forRecommender Systems
Abstract: Currently, deeplearning techniques have achieved great success in various areas such asmultimedia processing, pattern recognition, and natural language processing. Inrecent two years, some deep learning-based recommendation algorithms have beenproposed and received state-of-the-art results in different recommendationtasks. In this talk, firstly I some deep learning-based algorithms aresummarized. These papers were recently published in KDD, SIGIR, WWW, andRecSys, which utilized different deep learning methods to process multimedia,text and rating scores for recommendations. Then some possible directions inthis field as well as our current work will be introduced.
Bio: Shuaiqiang Wangis an Assistant Professor at University of Jyväskylä in Finland.He received Ph.D. and B.Sc. in Computer Science from Shandong University,China, in 2009 and 2004 respectively. He visited Hong Kong Baptist Universityas an exchange Ph.D. student at in 2009. He was a postdoctoral researchassociate at Texas State University in 2010, and an Associate Professor atShandong University of Finance and Economics from 2011 to 2014. His researchinterests include recommender systems, information retrieval and data mining.He has published more than 30 papers in leading conferences like SIGIR, AAAIand CIKM, and journals like TOIS, TKDE and TIST. He served as a PC member for anumber of conferences like SIGIR, IJCAI and CIKM, and a reviewer for journalslike TOIS, AIJ, TWEB and TEC. The detailed information can be found from hishomepage http://users.jyu.fi/~swang/.