Advertisement

An evolutionary clustering approach based on temporal aspects for context-aware service recommendation

  • Haithem Mezni
  • Sofiane Ait Arab
  • Djamal BenslimaneEmail author
  • Karim Benouaret
Original Research

Abstract

Over the last years, recommendation techniques have emerged to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation only consider the traditional user-service relation, while in the real world, the perception and popularity of Web services may depend on several conditions including temporal, spatial and social constraints. Such additional factors in recommender systems influence users’ preferences to a large extent. In this paper, we propose a context-aware Web service recommendation approach with a specific focus on time dimension. First, K-means clustering method is hybridized with a multi-population variant of the well-known Particle Swarm Optimization (PSO) in order to exclude the less similar users which share few common Web services with the active user in specific contexts. Slope One method is, then, applied to predict the missing ratings in the current context of user. Finally, a recommendation algorithm is proposed in order to return the top-rated services. Experimental studies confirmed the accuracy of our recommendation approach when compared to three existing solutions.

Keywords

Web service recommendation Context-aware clustering Multi-swarm optimization K-means Slope One 

Notes

References

  1. Adomavicius Gediminas, Tuzhilin Alexander (2015) Context-aware recommender systems. In: Recommender systems handbook. Springer, pp 191– 226Google Scholar
  2. Adomavicius Gediminas (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst (TOIS) 23(1):103–145CrossRefGoogle Scholar
  3. Alam Shafiq (2014) Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evolut Comput 17:1–13CrossRefGoogle Scholar
  4. Bouker Slim (2014) Mining undominated association rules through interestingness measures. Int J Artif Intell Tools 23(04):1460011CrossRefGoogle Scholar
  5. Campos Pedro G, Díez Fernando, Cantador Iván (2014) Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model User-Adapted Inter 24(1–2):67–119CrossRefGoogle Scholar
  6. Chu VW, Wong RK, Chi C, Chen F (2015) Web service recommendations based on time-aware Bayesian networks. In: Big Data (BigData Congress), 2015 IEEE International Congress. pp 359–366Google Scholar
  7. Costa A, Guizzardi R, Guizzardi Filho G (2007) “COReS: Contextaware ontology-based recommender system for service recommendation”. In: Proc. 19-th Intern. Conf. on Advanced Information Systems Engineering (CAISE07)Google Scholar
  8. Dalvi Nilesh, Suciu Dan (2007) Efficient query evaluation on probabilistic databases. VLDB J Int J Very Large Data Bases 16(4):523–544CrossRefGoogle Scholar
  9. Deng Z et al (2015) Twitter is faster: personalized time-aware video recommendation from Twitter to YouTube”. In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 11.2, p 31CrossRefGoogle Scholar
  10. Dey Anind K (2001) Understanding and using context. Personal Ubiquitous Comput 5(1):4–7CrossRefGoogle Scholar
  11. Fan X, Hu Y, Zhang R (2014) Context-aware web services recommendation based on user preference. In: Services computing conference (APSCC), 2014 Asia-Pacific. IEEE, pp. 55–61Google Scholar
  12. Fan X et al (2015) Modeling temporal effectiveness for contextaware web services recommendation. In: Web services (ICWS), 2015 IEEE International Conference on. IEEE, pp 225–232Google Scholar
  13. Fan Xiaoliang et al. (2017). “CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness”. In: IEEE Transactions on Services ComputingGoogle Scholar
  14. Formoso V, Cacheda F, Carneiro V (2008) Algorithms for efficient collaborative filtering. In: Efficiency issues in information retrieval workshop. Vol. 17Google Scholar
  15. George T, Merugu S (2005) A scalable collaborative filtering framework based on co-clustering. In: Data mining, Fifth IEEE international conference on IEEEGoogle Scholar
  16. Gonzalez-Pardo Antonio, Jung Jason J, Camacho David (2017) ACObased clustering for Ego network analysis. Future Gener Comput Syst 66:160–170CrossRefGoogle Scholar
  17. Hartigan JA, Wong MA (1979) Algorithm AS 136: a kmeans clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108Google Scholar
  18. Hu R, Dou W, Liu J (2012) A context-aware collaborative filtering approach for service recommendation. In: Cloud and service computing (CSC), 2012 International Conference on IEEE, pp 148–155Google Scholar
  19. Hu Y et al (2014) Time-aware collaborative filtering for QoS-based service recommendation. In: Web services (ICWS), 2014 IEEE International Conference, pp 265–272Google Scholar
  20. Hu Yan (2015) Time aware and data sparsity tolerant Web service recommendation based on improved collaborative filtering. IEEE Trans Serv Comput 8(5):782–794CrossRefGoogle Scholar
  21. Jannach Dietmar (2010) Recommender systems: an introduction. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  22. Jelassi Mohamed Nader, Yahia Sadok Ben, Nguifo Engelbert Mephu (2015) Towards more targeted recommendations in folksonomies. Social Netw Anal Mining 5(1):68CrossRefGoogle Scholar
  23. Kao Yi-Tung, Zahara Erwie, Kao I-Wei (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762CrossRefGoogle Scholar
  24. Kennedy J (2011) Particle swarm optimization. Encyclopedia of machine learning. Springer, New York, pp 760–766Google Scholar
  25. Khoshneshin M, Street WN (2010) Incremental collaborative filtering via evolutionary co-clustering. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp 325–328Google Scholar
  26. Kuang L, Xia Y, Mao Y (2012) Personalized services recommendation based on context-aware QoS prediction. In: Web services (ICWS), 2012 IEEE 19th International Conference on IEEE, pp 400–406Google Scholar
  27. Kumara B et al (2014) Recommendation for web services with domain specific context awareness. In: Computational intelligence and data mining (CIDM), 2014 IEEE Symposium on IEEE, pp 281–287Google Scholar
  28. Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM international conference on data mining. SIAM, pp 471–475Google Scholar
  29. Lin S, Tao X, Yu T (2011) Time-based slope one algorithm. In: International conference on mechanical and electrical technology, 3rd (ICMET-China 2011), Volumes 1–3. ASME PressGoogle Scholar
  30. Liu D, Meng XW, Chen JL (2008) A framework for context-aware service recommendation. In: Advanced communication technology. ICACT 2008. 10th International Conference on Vol. 3, pp 2131–2134Google Scholar
  31. Liu L et al (2010) Using context similarity for service recommendation. In: Semantic computing (ICSC), 2010 IEEE Fourth International Conference on IEEE, pp 277–284Google Scholar
  32. Liu L, Mehandjiev N, Xu L (2011) Using contextual information for service recommendation. In: System sciences (HICSS), 2011 44th Hawaii International Conference on IEEE, pp 1–9Google Scholar
  33. Liu Liwei, Mehandjiev Nikolay, Dong-Ling Xu (2013) Context similarity metric for multidimensional service recommendation. Int J Electr Commer. 18(1):73–104CrossRefGoogle Scholar
  34. Maamar Zakaria, Hacid Hakim, Huhns Michael N (2011) Why web services need social networks. IEEE Internet Comput 15(2):90–94CrossRefGoogle Scholar
  35. Menéndez Héctor D, Otero Fernando EB, Camacho D (2014) MACOC: a medoid-based ACO clustering algorithm. In: International conference on swarm intelligence. Springer, pp. 122–133Google Scholar
  36. Menéndez Héctor D, Otero Fernando EB, Camacho David (2016) Medoid-based clustering using ant colony optimization. Swarm Intell 10(2):123–145CrossRefGoogle Scholar
  37. Mezni H, Fayala M (2018) Time-aware service recommendation: taxonomy, review and challenges. In: Software: practice and experience.  https://doi.org/10.1002/spe.2605.
  38. Niknam Taher, Amiri Babak (2010) An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl Soft Comput 10(1):183–197CrossRefGoogle Scholar
  39. Omran Mahamed GH, Salman Ayed, Engelbrecht Andries P (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl 8(4):332MathSciNetCrossRefGoogle Scholar
  40. Petridou Sophia G et al. (2006) A divergence-oriented approach for web users clustering. In: International conference on computational science and its applications. Springer, pp 1229–1238Google Scholar
  41. Petridou Sophia G (2008) Time-aware web users’ clustering. IEEE Trans Knowl Data Eng 20(5):653–667CrossRefGoogle Scholar
  42. Rana Chhavi, Jain Sanjay Kumar (2014) An evolutionary clustering algorithm based on temporal features for dynamic recommender systems. Swarm Evolut Comput 14:21–30CrossRefGoogle Scholar
  43. Sun Zhoubao (2015) Recommender systems based on social networks. J Syst Softw 99:109–119CrossRefGoogle Scholar
  44. Tian G et al (2015) Integrating implicit feedbacks for time-aware web service recommendations. In: Information systems frontiers, pp 1–15CrossRefGoogle Scholar
  45. Tian G, Wang J, He K, Hung PCK, Sun C (2014) Time-aware web service recommendations using implicit feedback. In: Web services (ICWS), 2014 IEEE International Conference, Vol. 273–280Google Scholar
  46. Wong Raymond K, Chu Victor W, Hao Tianyong (2014) Online role mining for context-aware mobile service recommendation. Personal Ubiquitous Comput 18(5):1029–1046CrossRefGoogle Scholar
  47. Xu Xia (2015) Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. Appl Soft Comput 29:169–183CrossRefGoogle Scholar
  48. Yao Lina (2015) Unified collaborative and content-based web service recommendation. IEEE Trans Serv Comput 8(3):453–466CrossRefGoogle Scholar
  49. Yu Z, Wong R, Chi C-H (2015) Efficient role mining for context-aware service recommendation using a high-performance cluster. In: IEEE transactions on services computingGoogle Scholar
  50. Zhang Y, Zheng Z, Lyu MR (2011) WSPred: a time-aware personalized QoS prediction framework for Web services. In: Software reliability engineering (ISSRE), 2011 IEEE 22nd International Symposium on IEEE, pp 210–219Google Scholar
  51. Zhong Y et al (2014) Time-aware service recommendation for mashup creation in an evolving service ecosystem. In: Web services (ICWS), 2014 IEEE International Conference on IEEE, pp 25–32Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Haithem Mezni
    • 1
  • Sofiane Ait Arab
    • 2
  • Djamal Benslimane
    • 2
    Email author
  • Karim Benouaret
    • 2
  1. 1.JendoubaTunisia
  2. 2.University of LyonLyonFrance

Personalised recommendations