NCSI: Large-Scale Fashion Recommendation Systems Using TensorFlow and Spark

Status	Completed
Mentor Name	Hao Ji
Mentor's XSEDE Affiliation	Education Allocation
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Large-Scale Fashion Recommendation Systems Using TensorFlow and Spark
Summary	Recommender systems provide an essential means of recommending products to users that meet their needs or preferences, which have proven to be beneficial in e-commerce society. An important research problem in modern recommender systems is how to integrate discriminative content features into the recommendation process. In this project, we plan to: 1) investigate effective and automatic deep learning models using TensorFlow to identify fashion items in street fashion images; and 2) design large-scale fashion recommender systems in Spark to accommodate the use of fashion features learned by deep learning models.
Job Description	The students participating in this project will work on the following tasks: 1) detect and align fashion landmarks in each image; 2) use image classifier to identify fashion items in fashion images; 3) employ deep features in fashion recommender systems.
Computational Resources	XSEDE Bridges' GPUs, Hadoop, and Spark resources
Contribution to Community
Position Type	Apprentice
Training Plan	I will offer a "Machine Learning and Its Applications" course in the fall semester of 2018. This course will prepare students with the necessary knowledge and skills in Machine Learning. During this project, I will work closely with the students and plan to have weekly meetings with them to guide their research progress.
Student Prerequisites/Conditions/Qualifications	Must have an undergraduate at California State Polytechnic University, Pomona Must have a good understanding of linear algebra and good programming skills in Python. Scala is plus.
Duration	Semester
Start Date	08/23/2018
End Date	12/09/2018