Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Social Network-based Hybrid Collaborative Filtering using Genetic Algorithms

유전자 알고리즘을 활용한 소셜네트워크 기반 하이브리드 협업필터링

  • Noh, Heeryong (Graduate School of Business IT, Kookmin University) ;
  • Choi, Seulbi (Graduate School of Business IT, Kookmin University) ;
  • Ahn, Hyunchul (Graduate School of Business IT, Kookmin University)
  • 노희룡 (국민대학교 비즈니스IT전문대학원) ;
  • 최슬비 (국민대학교 비즈니스IT전문대학원) ;
  • 안현철 (국민대학교 비즈니스IT전문대학원)
  • Received : 2017.03.14
  • Accepted : 2017.03.20
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

본 연구는 사용자 평점 이외에 사용자 간 직접 간접적 신뢰 및 불신 관계 네트워크의 분석 결과를 추가로 반영한 새로운 하이브리드 협업필터링(Collaborative filtering, CF) 추천방법을 제안한다. 구체적으로 사용자 간의 유사도를 계산할 때 사용자 평가점수의 유사성만을 고려하는 기존의 CF와 다르게, 사용자 신뢰 및 불신 관계 데이터의 사회연결망분석 결과를 추가적으로 고려하여 보다 정교하게 사용자 간의 유사도를 산출하였다. 이 때, 사용자 간의 유사도를 재조정하는 접근법으로 특정 이웃 사용자가 신뢰 및 불신 관계 네트워크에서 높은 신뢰(또는 불신)를 받을 때, 추천 대상이 되는 사용자와 해당 이웃 간의 유사도를 확대(강화) 또는 축소(약화)하는 방안을 제안하고, 더 나아가 최적의 유사도 확대 또는 축소의 정도를 결정하기 위해 유전자 알고리즘(genetic algorithm, GA)을 적용하였다. 본 연구에서는 제안 알고리즘의 성능을 검증하기 위해, 특정 상품에 대한 사용자의 평가점수와 신뢰 및 불신 관계를 나타낸 실제 데이터에 추천 알고리즘을 적용하였으며 그 결과, 기존의 CF와 비교했을 때 통계적으로 유의한 수준의 예측 정확도 개선이 이루어짐을 확인할 수 있었다. 또한 신뢰 관계 정보보다는 불신 관계 정보를 반영했을 때 예측 정확도가 더 향상되는 것으로 나타났는데, 이는 사회적인 관계를 추적하고 관리하는 측면에서 사용자 간의 불신 관계에 대해 좀 더 주목해야 할 필요가 있음을 시사한다.

Keywords

References

  1. Adomavicious, G., and Tuzhilin, A., "Toward the Next Generation of Recommender Systems : A Survey of the State- of-the-art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, 2005. pp. 734-749. https://doi.org/10.1109/TKDE.2005.99
  2. Ahn, H., "Improvement of a Context-aware Recommender System through User's Emotional State Prediction," Journal of Information Technology Applications & Management, Vol. 21, No. 4, 2014, pp. 203-223.
  3. Ahn H., and Lee, H. Y., "A Combination Model of Multiple Artificial Intelligence Techniques Based on Genetic Algorithms for Investment Decision Support Aid : An Application to KOSPI," The e-Business Studies, Vol. 10, No. 1, 2009, pp. 267-288.
  4. Ahn, S.-M., Kim, I. H., Choi, B., Cho, Y., Kim, E., and Kim, M.-K., "Understanding the Performance of Collaborative Filtering Recommendation through Social Network Analysis," Journal of Society for e-Business Studies, Vol. 17, No. 2, 2012, pp. 129-147. https://doi.org/10.7838/JSEBS.2012.17.2.129
  5. Ahn, Y. Y., Han, S., Kwak, H., Moon, S., and Jeong, H., "Analysis of Topological Characteristics of Huge Online Social Network Service," Proceeding of 16th International Conference on World Wide Web, 2007, pp. 835-844.
  6. Barnes, J. A., "Class and committees in a Norwegian island parish," Human Relations, Vol. 7, 1954, pp. 39-58. https://doi.org/10.1177/001872675400700102
  7. Breese, J. S., Heckerman, D., and Kadie, C., "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), San Francisco, California, 1998, pp. 43-52.
  8. Burke, R. "Hybrid recommender systems: Survey and experiments," User Modeling and User-adapted Interaction, Vol. 12, No. 4, 2002, pp. 331-370. https://doi.org/10.1023/A:1021240730564
  9. Burke, R. "Hybrid web recommender systems," The Adaptive Web, pp. 377-408.
  10. Cho, Y. H. and Kim, J. K., "Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-Commerce," Expert Systems with Applications, Vol. 26, No. 2, 2004, pp. 233-246. https://doi.org/10.1016/S0957-4174(03)00138-6
  11. Cho, Y. H., and Bang, J., "Social Network Analysis for New Product Recommendation," Journal of Intelligence and Information Systems, Vol. 15, No. 4, 2009, pp. 183-200.
  12. Choi, S., Kwahk, K. -Y., and Ahn, H., "Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users," Journal of Intelligence and Information Systems, Vol. 22, No. 3, 2016, pp. 113-127. https://doi.org/10.13088/jiis.2016.22.3.113
  13. Du, Y., Du, X., and Huang, L. "Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction," Chinese Journal of Electronics, Vol. 25, No. 3, 2016, pp. 418-423. https://doi.org/10.1049/cje.2016.05.005
  14. Ganu, G., Elhadad, N., and Marian, A. "Beyond the Stars: Improving Rating Predictions using Review Text Content," WebDB, 2009.
  15. Golbeck, J., "Generating predictive movie recommendations from trust in social networks," Proceedings of the 4th International Conference on Trust Management, Lecture Notes in Computer Science, Vol. 3986, 2006, pp. 93-104.
  16. Ha, I., User Modeling-based Recommender System with Link Attributes of Trust Network, Ph.D. Dissertation, Inha University, 2014.
  17. Hahn, K. H., and Kim, J., "The effect of offline brand trust and perceived internet confidence on online shopping intention in the integrated multi-channel context," International Journal of Retail and Distribution Management, Vol. 37, 2009, pp. 126-141. https://doi.org/10.1108/09590550910934272
  18. Hong, S. -H., and Shin, K. -S., "Using GA based Input Selection Method for Artificial Neural Network Modeling: Application to Bankruptcy Prediction," Journal of Intelligence and Information Systems, Vol. 9, No. 1, 2003, pp. 227-249.
  19. Hurlye, R. F., The Decision to Trust: How Leaders Create High-trust Organization, San francisco, 2011, CA: Jossey-Bass.
  20. Jeon, B., and Ahn, H., "A Collaborative Filtering System Combined with Users' Review Mining: Application to the Recommendation of Smartphone Apps," Journal of Intelligence and Information Systems, Vol. 21, No. 2, 2015, pp. 1-18. https://doi.org/10.13088/jiis.2015.21.2.01
  21. Jeong, J.-H., and Kim, J.-W., "Collaborative Filtering Techniques Using Social Network Analysis for UCC Recommendation", Journal of Intelligence and Information Systems, Vol. 11, No. 1, 2013, pp. 185-195.
  22. Kim, Y. H., Social Network Analysis, Parkyoungsa Publishing, 2003.
  23. Kwahk, K. Y., Social Network Analysis, Cheongram, 2014.
  24. Lee, J. S. and S. D. Park, "Performance Improvement of a Movie Recommendation System using Genre-wise Collaborative Filtering," Journal of Intelligence and Information Systems, Vol. 13, No. 4, 2007, pp. 65-78.
  25. Ling, G., Lyu, M. R., and King, I. "Ratings meet reviews, a combined approach to recommend," Proceedings of the 8th ACM Conference on Recommender Systems, 2014, pp. 105-112.
  26. Liu, F., and Lee, H. J., "Use of social network information to enhance collaborative filtering performance," Expert Systems with Applications, Vol. 37, No. 7, 2010, pp. 4772-4778. https://doi.org/10.1016/j.eswa.2009.12.061
  27. Lu, Y., Yang, S., Chau, P. Y. K., and Can, Y., "Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective," Information & Management, Vol. 48, No. 8, pp. 393-403, 2011. https://doi.org/10.1016/j.im.2011.09.006
  28. Massa, P., Avesani, P.: "Trust-aware Collaborative Filtering for Recommender Systems," Proceedings of Federated Int. Conference on the Move to Meaningful Internet, 2004, pp. 492-508.
  29. Mayer, R. C., Davis, J. H., and Schoorman, F. D., "An Integrative Model of Organizational Trust," Academy of Management Review, Vol. 30, 1995, pp. 709-734.
  30. McAuley, J. and Leskovec, J. "Hidden factors and hidden topics: Understanding rating dimensions with review text," Proceedings of the 7th ACM Conference on Recommender Systems, 2013, pp. 165-172.
  31. McNee, S. M., Kapoor, N., and Konstan, J. A., "Don't look stupid: avoiding pitfalls when recommending research papers," Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work, 2006, pp. 171-180.
  32. Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B., "Measurement and Analysis of Online Social Networks," Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, 2007, pp. 29-42.
  33. Noh, H., and Ahn, H. "A Study on the Recommendation Algorithm based on Trust/Distrust Relationship Network Analysis," Journal of Information Technology Application & Management, Vol. 24, No. 1, 2017, pp. 169-185.
  34. Ryu, Y. U., Kim, H. K., Cho, Y. H., and Kim, J. K., "Peer-oriented content recommendation in a social network," Proceedings of the Sixteenth Workshop on Information Technologies and Systems, 2006, pp. 115-120.
  35. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Application of dimensionality reduction in recommender systems: A case study," Proceedings of the WebKDD Workshop at the ACM SIGKKD, 2000.
  36. Sarwar, B., Karypis G., Konstan, J., and Riedl, J., "Item-based collaborative filtering recommendation algorithms," Proceeding of the 10th International Conference on World Wide Web, 2001, pp. 285-295.
  37. Schafer, J. B., Konstan, J. A., and Riedl, J., "E-commerce Recommendation Applications," Data Mining and Knowledge Discovery, Vol.5, Nos. 1-2, 2001, pp. 115-153. https://doi.org/10.1023/A:1009804230409
  38. Shin, C.-H., Lee, J.-W., Yang, H.-N., and Choi, I. Y., "The Research on Recommender for New Customers Using Collaborative Filtering and Social Network Analysis," Journal of Intelligence and Information Systems, Vol. 18, No. 4, 2012, pp. 19-42. https://doi.org/10.13088/JIIS.2012.18.4.019
  39. Son, J., Kim, S. B., Kim, H., and Cho, S. "Review and Analysis of Recommender Systems," Journal of the Korean Institute of Industrial Engineers, Vol. 41, No. 2, 2015, pp. 185-208. https://doi.org/10.7232/JKIIE.2015.41.2.185
  40. Tang, J., Aggarwal, C., and Liu, H., "Recommendations in signed social networks," Proceedings of the 25th International Conference on World Wide Web, 2016, pp. 31-40.
  41. Thelwall, M., "Social Networks, Gender, and Friending: An Analysis of MySpace Member Profiles," Journal of the American Society for Information Science and Technology, Vol. 59, No. 8, 2008, pp. 1321-1330. https://doi.org/10.1002/asi.20835
  42. Vozalis, M., and Margaritis, K. G. "Collaborative filtering enhanced by demographic correlation," AIAI Symposium on Professional Practice in AI, of the 18th World Computer Congress, 2004.
  43. Wang, P., Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A. L., "Understanding the Spreading Patterns of Mobile Phone Viruses," Science, Vol. 324, 2009, pp. 1071-1076. https://doi.org/10.1126/science.1167053
  44. Wei, C., Richard K., and Simon F., "Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm," Information Systems Frontiers, Vol. 15, No. 4, 2013, pp. 533-551. https://doi.org/10.1007/s10796-012-9377-6
  45. Yuan, W., Improved Trust-Aware Recommender System using Small-Worldness of Trust Networks, Ph.D. Dissertation, Kyung Hee University, 2010.

Cited by

  1. 네트워크 중심성 척도가 추천 성능에 미치는 영향에 대한 연구 vol.27, pp.1, 2021, https://doi.org/10.13088/jiis.2021.27.1.023