An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators

  • Oommen, B. John (School of Computer Science, Carleton University) ;
  • Yazidi, Anis (Department of ICT, University of Agder) ;
  • Granmo, Ole-Christoffer (Department of ICT, University of Agder)
  • Received : 2012.03.16
  • Accepted : 2012.04.09
  • Published : 2012.06.30


Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.


  1. A. Aghasaryan, S. Betgé-Brezetz, C. Senot, and Y. Toms, "A profiling engine for converged service delivery platforms", Bell Lab. Tech. J., Vol.13, No.2, 2008, pp.93-103.
  2. M. Basseville and I. V. Nikiforov, Detection of abrupt changes: theory and application. Prentice-Hall, Inc., 1993.
  3. M. Brunato and R. Battiti, "Pilgrim: A location broker and mobility-aware recommendation system", in PERCOM '03: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, Washington, DC, USA, 2003, p.265.
  4. U. Bud and J. Lim, "Distance functions to detect changes in data streams", JIPS, Vol.2, No.1, 2006, pp.44-47.
  5. D. Godoy and A. Amandi, "User profiling in personal information agents: a survey", Knowl. Eng. Rev., Vol.20, 2005, pp.329-361.
  6. D. Godoy, S. Schiaffino, and A. Amandi, "Interface agents personalizing web-based tasks", Cognitive Systems Research, Special Issue on Intelligent Agents and Data Mining for Cognitive Systems, Vol.5, No.3, 2004, pp.207-222.
  7. M. A. Hossain, P. K. Atrey, and A. El Saddik, "Gain-based selection of ambient media services in pervasive environments", Mob. Netw. Appl., Vol.13, No.6, 2008, pp.599-613.
  8. M. A. Hossain, J. Parra, P. K. Atrey, and A. El Saddik, "A framework for human-centered provisioning of ambient media services", Multimedia Tools and Applications, Vol.44, 2009, pp.407-431.
  9. Y. Kim, W. Kim, and U. Kim, "Mining frequent itemsets with normalized weight in continuous data streams", JIPS, Vol.6, No.1, 2010, pp.79-90.
  10. I. Koychev, "Gradual forgetting for adaptation to concept drift", in Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning, 2000, pp.101-106.
  11. I. Koychev and R. Lothian, "Tracking drifting concepts by time window optimisation", in Research and Development in Intelligent Systems XXII, M. Bramer, F. Coenen, and T. Allen, Eds. Springer London, 2006, pp.46-59.
  12. I. Koychev and I. Schwab, "Adaptation to drifting user's interests", in Proceedings of ECML2000 Workshop: Machine Learning in New Information Age, 2000, pp.39-46.
  13. S. Kurkovsky and K. Harihar, "Using ubiquitous computing in interactive mobile marketing", Personal Ubiquitous Comput., Vol.10, No.4, 2006, pp.227-240.
  14. J.-W. Lee and Y.-J. Lee, "A knowledge discovery framework for spatiotemporal data mining", JIPS, Vol.2, No.2, 2006, pp.124-129.
  15. M. A. Maloof and R. S. Michalski, "Selecting examples for partial memory learning", Machine Learning, Vol.41, 2000, pp.27-52.
  16. S. E. Middleton, N. R. Shadbolt, and D. C. De Roure, "Ontological user profiling in recommender systems", ACM Trans. Inf. Syst., Vol.22, No.1, 2004, pp.54-88.
  17. T. M. Mitchell, R. Caruana, D. Freitag, J. McDermott, and D. Zabowski, "Experience with a learning personal assistant", Commun. ACM, Vol.37, No.7, 1994, pp.80-91.
  18. M. Montaner, B. López, and J. L. de la Rosa, "A taxonomy of recommender agents on the internet,'' Artificial Intelligence Review, Vol.19, 2003, pp.285-330.
  19. K. S. Narendra and M. A. L. Thathachar, Learning Automata: An Introduction. 1em plus Prentice Hall, 1989.
  20. Y. Naudet, A. Aghasaryanb, S. Mignon, Y. Toms, and C. Senot, "Ontology-based profiling and recommendations for mobile tv,'' in Semantics in Adaptive and Personalized Services, ser. Studies in Computational Intelligence, M. Wallace, I. Anagnostopoulos, P. Mylonas, and M. Bielikova, Eds. Springer, Heidelberg, 2010, Vol.279, pp.23-48.
  21. I. Ong and H. Lim, "Dynamic load balancing and network adaptive virtual storage service for mobile appliances", JIPS, Vol.7, No.1, 2011, pp.53-62.
  22. B. J. Oommen and S. Misra, "Fault-tolerant routing in adversarial mobile ad hoc networks: an efficient route estimation scheme for non-stationary environments", Telecommunication Systems, Vol.44, 2010, pp.159-169.
  23. B. J. Oommen and L. Rueda, "Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments", Pattern Recogn., Vol.39, No.3, 2006, pp.328-341.
  24. L. Rueda and B. J. Oommen, "Stochastic automata-based estimators for adaptively compressing files with nonstationary distributions", Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, Vol.36, No.5, 2006, pp.1196-1200.
  25. A. Saeed, M. Faezipour, M. Nourani, S. Banerjee, G. Lee, G. Gupta, and L. Tamil, "A scalable wireless body area network for bio-telemetry", JIPS, Vol.5, No.2, 2009, pp.77-86.
  26. A. N. Shiryayev, Optimal Stopping Rules. Springer, 1978.
  27. A. Stensby, B. J. Oommen, and O.-C. Granmo, "Language detection and tracking in multilingual documents using weak estimators", in Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition, ser. SSPR&SPR'10, 2010, pp.600-609.
  28. M. A. L. Thathachar and P. S. Sastry, Networks of Learning Automata: Techniques for Online Stochastic Optimization. Kluwer Academic Publishers, 2004.
  29. G. Widmer, "Tracking context changes through meta-learning", Mach. Learn., Vol.27, No.3, 1997, pp.259-286.
  30. W.-S. Yang, H.-C. Cheng, and J.-B. Dia, "A location-aware recommender system for mobile shopping environments", Expert Syst. Appl., Vol.34, No.1, pp.437-445, 2008.
  31. A. Yazidi, O.-C. Granmo, B. J. Oommen, M. Gerdes, and F. Reichert, "A user-centric approach for personalized service provisioning in pervasive environments", Submitted for Publication.
  32. F. Yu, D. Oyana, W.-C. Hou, and M. Wainer, "Approximate clustering on data streams using discrete cosine transform", JIPS, Vol.6, No.1, 2010, pp.67-78.
  33. Z. Yu, X. Zhou, D. Zhang, C.-Y. Chin, X. Wang, and J. Men, "Supporting context-aware media recommendations for smart phones", IEEE Pervasive Computing, Vol.5, July 2006, pp.68-75.

Cited by

  1. Semisupervised Location Awareness in Wireless Sensor Networks Using Laplacian Support Vector Regression vol.10, pp.4, 2014,
  2. Self-Organized Cognitive Sensor Networks: Distributed Channel Assignment for Pervasive Sensing vol.10, pp.3, 2014,
  3. Ontology-based library recommender system using MapReduce vol.18, pp.1, 2015,
  4. Antistray, Learning Smart: Creating Indoor Positioning Learning Environment for Augmenting Self-Regulated Learning vol.10, pp.4, 2014,
  5. A Socially Aware Routing Based on Local Contact Information in Delay-Tolerant Networks vol.2014, 2014,
  6. Relative weight evaluation of the factors inducing social media service use vol.74, pp.14, 2015,
  7. User tailored cloud-learning system using SNS and learning resources vol.74, pp.14, 2015,
  8. Developing a Mobile Learning System in Augmented Reality Context vol.9, pp.12, 2013,
  9. A smart assistant toward product-awareness shopping vol.18, pp.2, 2014,
  10. Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments vol.60, 2016,
  11. Smart CDSS: integration of Social Media and Interaction Engine (SMIE) in healthcare for chronic disease patients vol.74, pp.14, 2015,
  12. Distributed and Parallel Big Textual Data Parsing for Social Sensor Network vol.9, pp.12, 2013,
  13. The QoS-based MCDM system for SaaS ERP applications with Social Network vol.66, pp.2, 2013,
  14. Correcting vindictive bidding behaviors in sponsored search auctions vol.69, pp.3, 2014,
  15. Improved user similarity computation for finding friends in your location vol.8, pp.1, 2018,
  16. Parameter estimation in abruptly changing dynamic environments using stochastic learning weak estimator vol.48, pp.11, 2018,