Projects

Recommendation System

Given who-watched-which TV transaction data, how can we recommend relevant TV programs for a given user? Given a frienship social network, how can we recommend friends that are likely to make connections to a given user? Recommendation is an important application of data mining, and is widely used in movie recommendation, restaurant recommendation, job recommenation, article recommendation, and friend recommendation. In this project, we work on designing and developing models, algorithms, and systems for recommendation. We focus on the following two types of data.

  • Matrix recommendation: most data for recommendation are expressed in a matrix form; e.g., user and movie information in movie recommendation is expressed in a matrix form. We work on models and algorithms for scalable recommendation.
  • Graph recommendation: we work on recommendation in graphs (e.g., "People You May Know" in LinkedIn, or friend recommendation in Facebook) which is a very important problem. We work on fast and scalable models and algorithms for graph recommendation.

Applications:

  • Movie/TV program recommendation
  • Restaurant recommendation
  • Friend recommendation
  • News/Article recommendation

Publication

  • Minji Yoon, Jinhong Jung, and U Kang, "TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs", 34th IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France. [PDF]
  • Minji Yoon, Woojeong Jin, and U Kang, "Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees", The Web Conference (WWW) 2018, Lyon, France. [PDF] [BIBTEX]
  • Haekyu Park, Jinhong Jung, and U Kang, "A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems", IEEE International Conference on Big Data (BigData) 2017, Boston, MA, USA. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Kijung Shin, Lee Sael, and U Kang, "Fully Scalable Methods for Distributed Tensor Factorization", IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 29, no. 1, pp. 100-113, Jan. 1 2017. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Jinhong Jung, Namyong Park, Lee Sael, and U Kang, "BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart", ACM International Conference on Management of Data (SIGMOD) 2017, Raleigh, North Carolina, USA. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Jinhong Jung, Woojeong Jin, Lee Sael, and U Kang, "Personalized Ranking in Signed Networks using Signed Random Walk with Restart", IEEE International Conference on Data Mining (ICDM) 2016, Barcelona, Spain. [BIBTEX] [PDF] [HOMEPAGE (CODE, DATA)]
  • Jinhong Jung, Kijung Shin, Lee Sael, and U Kang, "Random Walk with Restart on Large Graphs Using Block Elimination", ACM Transactions on Database Systems (TODS), vol. 41, issue 2, pp. 12:1-12:43, June 2016. [BIBTEX] [PDF] [HOMEPAGE (CODE, DATA)]
  • Kijung Shin, Jinhong Jung, Lee Sael, and U Kang, "BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs", ACM International Conference on Management of Data (SIGMOD) 2015, Melbourne, Australia [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Kijung Shin, and U Kang, "Distributed Methods for High-dimensional and Large-scale Tensor Factorization", IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Dongyeop Kang, DongGyun Han, NaHea Park, Sangtae Kim, U Kang, and Soobin Lee, "Eventera: Real-time Event Recommendation System from Massive Heterogeneous Online Media", IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China. [BIBTEX] [PDF]