Haekyu Park (박해규)


Research Intern at Data Mining Lab
Computer Science and Engineering, Seoul National University
Research Interests: Data Mining, Graph Data Mining, Machine Learning, Interpretable Machine Learning
Email: hkpark627@snu.ac.kr, Curriculum Vitae: Here!

Hello! I am a research intern at Data Mining Lab, advised by Prof. U Kang. I received B.S. in department of computer science and engineering at Seoul National University.

I am interested in drawing out core insight from intertwined and inundated data these days and utilize the data more creatively. My research currently focuses on machine learning and data mining. You can see my related research works below.

What's New

Education

Seoul National University

B.S., Computer Science and Engineering
Graduated with honors (Cum Laude)
Courses: Computer Vision, Introduction to Data Mining, Probabilistic Graphical Models, Software Engineering, Creative Integrated Design, System Programming, Operating Systems, Data Communications, Algorithms, Programming Language, Principles of Programming, IT Leadership Seminar, Computer Engineering Seminar, Electrical and Electronic Circuits, Computer Programming, Data Structures, Programming Practice, Discrete Mathematics, Logic Design, Logic Design Lab., Digital Computer Concept and Practice
Mar. 2012 - Aug. 2017

Publication

2018

Conference [2], WWW'18

SIDE: Representation Learning in Signed Directed Networks

Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang
The Web Conference (Previously known as WWW, World Wide Web Conference) 2018, Lyon, France
[PDF] [Website] [Details]

We propose SIDE, a network embedding algorithm for signed directed networks. Network embedding learns a mapping of each node to a vector. SIDE carefully formulates and optimizes likelihood over both direct and indirect signed connections. We provide socio-psychological interpretation for each component of likelihood function and prove linear scalability of our algorithm.
Preprint [1], arXiv

UniWalk: Explainable and Accurate Recommendation for Rating and Network Data

Haekyu Park, Hyunsik Jeon, Junghwan Kim, Beunguk Ahn, and U Kang
ArXiv
[PDF] [Website] [arXiv] [Details]
  • Main Question:
    How can we leverage social network data and observed ratings to correctly recommend proper items and provide a persuasive explanation for the recommendations?
  • Proposed Model:
    We propose UniWalk, an explainable and accurate recommender system that exploits both social network and rating data. UniWalk combines both data into a unified graph, learns latent features of users and items, and recommends items to each user through the features. Importantly, it explains why items are recommended together with the recommendation results.
    Latest Products Image
    We recommend items (a, b, and c) to a target user (u1).
    Latest Products Image
    We recommend the items because the items are preferred by other users (u2, u3, u4, and u5) who are similar to the target user.
    Latest Products Image
    We recommend the items because the target user likes other similar items (d, e, and i).

2017

Conference [1], BigData'17

A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems

Haekyu Park, Jinhong Jung, and U Kang
IEEE International Conference on Big Data (BigData) 2017, Boston, MA, USA
[PDF] [Website] [Slides] [Details]
  • Main Question:
    Between matrix factorization (MF) or Random Walk with Restart (RWR), which method works better for recommender systems?
  • Specific tasks:
    We compare MF and RWR for the following recommendation scenarios.
    • Which method performs better when using explicit feedback data?
    • → MF is better.
    • Which method performs better when using implicit feedback data?
    • → RWR is better.
    • Do global bias terms improve performance?
    • → Yes.
    • Which method performs better when exploiting global bias terms?
    • → MF is better.
    • Does side information enhance performance?
    • → Yes with explicit ratings, and No with implicit ratings.
    • Which method performs better when employing side information?
    • → MF is better with explicit rating data, and RWR is better with implicit rating data.
    • Which method solves the cold start problem better when employing side data?
    • → MF is better with explicit rating data, and RWR is better with implicit rating data.
  • Discussion:
    What are the reasons for good or bad performance of MF and RWR in various settings of recommendations? Details are in Section 4.G in our paper. Please read that part!

Project

2017

Recommender System on Videos of Oksusu Application

  • With SK Telecom, Seoul, Republic of Korea
  • Keywords: Tensorflow, Feedforward Neural Network, Sequence Embedding, Word Embedding, Approx. k-NN
  • [Details]
    We recommend videos to users in Oksusu application. Our model uses deep learning to achieve quality recommendation. Our recommendation engine handles massive data on users' behaviors and heterogeneous information on videos.

Aug 2017 - Present

A Fast and Cost Efficient Data Compression Algorithm with Shared Virtual Memory in Heterogeneous System Architecture

  • For my undergraduate graduation essay
  • Keywords: OpenCL, GPGPU, SVM, HSA
  • [Details]
    • I used GPGPU and Shared Virtual Memory (SVM) in Heterogeneous System Architecture (HSA).
    • I acquired parallel programming thinking and skills, especially with C++ and OpenCL.
    • We propose fast data deduplication methods that use general purpose computing on graphics processing units (GPGPU) and heterogeneous system architecture (HSA) to efficiently execute the great amount of computations. GPGPU and HSA provide a powerful basis for parallel computing in an easy programmable and efficient way.

Nov 2016 - Aug 2017

2016

Personalized Recommendation on Credit Card Rewards

  • With Hyundai Card, Seoul, Republic of Korea
  • Keywords: Coupled Matrix Factorization, Time Series Data
  • [Details]
    We provide personalized recommendations on credit card rewards to customers. The main algorithm is TCMF (Time Coupled Matrix Factorization). We use various side information of users and items and highly improve the recommendation performance.
  • [News article (in Korean)]

June - Dec, 2016

Social Recommender System with Graph and Rating Information

  • For final project of Probabilistic Graphical Model course
  • Keywords: Matrix Factorization, Network Embedding, Social Network, PGM
  • [Details]
    We designed and implemented a novel recommendation model leveraging social network by network embedding.

Sep - Dec, 2016

Awards & Honors

  • National Scholarship for Science and Engineering

    Merit-based, 2015

Research Experience

Technical Skills

  • Programming Languages

    • Advanced: Python, Java, C, C++
    • Experienced: Matlab, Ocaml, Scheme, HTML
  • Numerical Computing

    • Advanced: Numpy, SciPy, scikit-learn
    • Experienced: Matlab
  • Parallel Computing

    • Experienced: OpenCL

Patents

  • Apparatus and Method for Representation Learning in Signed Directed Networks

    U Kang, Junghwan Kim, and Haekyu Park, Korean Patent 10-2017-0130914, 2017.
  • Explainable and Accurate Recommender Method and System using Social Network Information and Rating Information

    U Kang, Haekyu Park, Junghwan Kim, and Hyunsik Jeon, Korean Patent 10- 2017-0159167, 2017.

Getting in touch

I would like to talk to you! Please contact me, through
  • Email: hkpark627@snu.ac.kr
  • Address: Data Mining Laboratory, Building 138 #314, Seoul National University 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea 08826
 Last update: Jan 25, 2018