The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Fast Random Walk with Restart over a Signed Graph

부호 그래프에서의 빠른 랜덤워크 기법

  • Myung, Jaeseok (Samsung Electronics Co.) ;
  • Shim, Junho (Division of Computer Science, Sookmyung Women's University) ;
  • Suh, Bomil (Division of Business Administration, Sookmyung Women's University)
  • Received : 2015.05.08
  • Accepted : 2015.05.20
  • Published : 2015.05.31
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Abstract

RWR (Random Walk with Restart) is frequently used by many graph-based ranking algorithms, but it does not consider a signed graph where edges may have negative weight values. In this paper, we apply the Balance Theory by F. Heider to RWR over a signed graph and propose a novel RWR, Balanced Random Walk (BRW). We apply the proposed technique into the domain of recommendation system, and show by experiments its effectiveness to filter out the items that users may dislike. In order to provide the reasonable performance of BRW in the domain, we modify the existing Top-k algorithm, BCA, and propose a new algorithm, Bicolor-BCA. The proposed algorithm yet requires employing a threshold. In the experiment, we show how threshold values affect both precision and performance of the algorithm.

랜덤워크는 그래프 기반의 랭킹 기법들에서 빈번히 사용되지만, 그래프 간선에 음수 가중치를 가지는 부호 그래프는 고려하지 않는다. 이 논문에서는 하이더의 균형 이론을 적용하여 랜덤워크수행 시 음수 가중치를 처리하는 기법을 제안한다. 제안 기법은 추천 시스템에 적용되었으며, 사용자가 선호하지 않는 아이템을 걸러내는 데 효과가 있음을 실험을 통해 보인다. 제안한 모델의 성능을 위해 기존의 Top-k 랜덤워크 계산 기법인 BCA를 확장한 Bicolor-BCA 알고리즘을 제안한다. 제안 알고리즘은 임계값이 필요한데, 실험을 통해 임계값에 따른 정확도와 성능의 변화를 살펴본다.

Keywords

References

  1. Avrachenkov, K., Litvak, N., Nemirovsky, D. A., Smirnova, E., and Sokol, M., "Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation," INRIA Research Report No.7367, 2010.
  2. Bahmani, B., Chakrabarti, K., and Xin, D., "Fast Personalized PageRank on MapReduce," Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 2011.
  3. Balmin, A. and Hristidis, V., "ObjectRank: authority-based keyword search in databases," Proceedings of the Thirtieth international conference on Very large data bases, 2004.
  4. Berkhin, P., "Bookmark-coloring approach to personalized pagerank com puting," Internet Mathematics, 2006.
  5. Chakrabarti, S., Pathak, A., and Gupta, M., "Index Design and Query Processing for Graph Conductance Search," The VLDB Journal, 2011.
  6. Clements, M., Vries, A. P., and Reinders, M. J. T., "Exploiting Positive and Negative Graded Relevance Assessments for Content Recommendation," Algorithms and Models for the Web-Graph, Lecture Notes in Computer Science, 2009.
  7. Fogaras, D., Racz, B., Csalogany, K., and Sarlos, T., "Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments," Internet Mathematics, 2005.
  8. Fusiwara, Y., Nakatsuji, M., Yamamuro, T., Shiokawa, H., and Onizuka, M., "Efficient Personalized PageRank with Accuracy Assurance," Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data minin, 2012.
  9. Heider, F., The Psychology of Interpersonal Relations, John Wiley & Sons, 2013.
  10. Jeh, G. and Widom, J., "SimRank: a measure of structural-context similarity," Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002.
  11. Kang, S. and Shim, J., "Empirical Analysis on the Shortcut Benefit Function and its Factors for Triple Database," Journal of Society for e-Business Studies, Vol 19, No 1, Society for e-Business Studies, 2014.
  12. MovieLens, http://grouplens.org/datasets/movielens/.
  13. Page, L., Brin, S., Motwani, R., and Winograd, T., "The PageRank Citation Ranking: Bringing Order to the Web," Technical Report, Stanford InfoLab, http://ilpubs.stanford.edu:8090/422/.
  14. Su, X. and Khoshgoftaar, T. M., "A Survey of Collaborative Filtering Techniques," Journal Advances in Artificial Intelligence, 2009.
  15. Tong, H., Faloutsos, C., and Pan, J., "Fast Random Walk with Restart and Its Applications," http://repository.cmu.edu/compsci/537/, 2006.

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  1. 청취 순서 성향을 고려한 랜덤워크 음악 추천 기법과 실험 사례 vol.22, pp.3, 2015, https://doi.org/10.7838/jsebs.2017.22.3.075