Research

Recommendation System

Given who-watched-which TV transaction data, how can we recommend relevant TV programs for a given user? Given a friendship 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 recommendation, 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 researches.

  • Recommendation in multi-modality, where multi-modal data, including ratings, social networks, texts, images, and videos are available.
  • Sequence recommendation where we want to predict the next item in a sequence (e.g., video and news recommendation).
  • Active recommendation: we devise method to "control" the dynamics of recommender systems, instead of merely observing them.
  • Network based recommendation: we work on recommendation in networks or 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 network based recommendation.

Applications:

  • Online market product recommendation
  • Movie/TV program recommendation
  • Restaurant recommendation
  • Friend recommendation
  • News/Article recommendation

Publication

  • Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, and U Kang, "Accurate Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating", The Web Conference (WWW), 2024, Singapore.
  • Hyunsik Jeon, Jun-gi Jang, Taehun Kim, and U Kang, "Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters", PLOS ONE (PLOS ONE), 2023 [PDF] [BIBTEX] [HOMEPAGE]
  • Hyunsik Jeon, Jongjin Kim, Jaeri Lee, Jong-eun Lee, and U Kang, "Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking", Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2023, Osaka, Japan. [PDF] [BIBTEX]
  • Jongjin Kim, Hyunsik Jeon, Jaeri Lee, and U Kang, "Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation", Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2023, Osaka, Japan. [PDF] [BIBTEX] [HOMEPAGE]
  • Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon, Jaeri Lee, and U Kang, "Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge", ACM International Conference on Information and Knowledge Management (CIKM), 2022, Atlanta, Georgia, USA. [PDF] [BIBTEX]
  • Bonheon Gu, Hyunsik Jeon, and U Kang, "PGT: News Recommendation Coalescing Personal and Global Temporal Preferences", Knowledge and Information Systems (KAIS), 2021 [PDF]
  • Bonheon Gu, Hyunsik Jeon, and U Kang, "Accurate News Recommendation Coalescing Personal and Global Temporal Preferences", Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020, Singapore. [PDF]
  • Hyunsik Jeon, Bonhun Koo, and U Kang, "Data Context Adaptation for Accurate Recommendation with Additional Information", IEEE International Conference on Big Data (BigData) 2019, Los Angeles, USA. [HOMEPAGE] [PDF]
  • Woojeong Jin, Jinhong Jung, and U Kang, "Supervised and Extended Restart in Random Walks for Ranking and Link Prediction in Networks", PLOS ONE (PLOS ONE), 2019. [PDF] [BIBTEX] [HOMEPAGE]
  • 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. [BIBTEX] [HOMEPAGE] [PDF]
  • 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. [BIBTEX] [HOMEPAGE] [PDF]
  • 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]
  • 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]
  • 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]
  • Jungeun Kim, Minsoo Choy, Daehoon Kim, and U Kang, "Link Prediction Based on Generalized Cluster Information", 23rd International World Wide Web Conference (WWW) 2014, Seoul, Korea. [BIBTEX] [PDF]