Total views : 294

A Recommender System for Improved Web Usage Mining and Personalization based on Foraging behavior based Swarm Intelligence


  • Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, India
  • Department of Computer Science and Engineering, SRM University, Kattankulathur,Chennai - 603203, Tamil Nadu, India


Objectives: The core intent of this paper is to propose a dynamic recommender system(i) to endow with an enhanced understanding of the behaviors and interest of online users and (ii) to tumble information overload by providing guidance to the online users. Methods: Foraging behavior based swarm intelligence is motivated from the dynamic social behavior of swarms. It puts forward innate option for modeling dynamic online usage data. In the preset work, we mainly concentrate on the innate resemblance between Swarm Intelligence and communal behavior. The proposed WebFrieseBee algorithm is inspired from the foraging behavior of the T. biroiFriese bees and the proposed the proposed BestProphecy-annealing algorithm is used for providing recommendations to users. Findings: The proposed WebFrieseBee is motivated from the dynamic social behavior of swarms. It offers collaborative learning and has decentralized control. Moreover, it also has high exploration ability. That is, it put forward innate option for modeling dynamic online usage data. The proposed BestProphecy-annealing algorithm uses a greedy heuristic approach, for providing recommendations to users by identifying a better neighborhood for agents and gives recommendations based on the preferences of these best neighborhoods. The proposed WebFrieseBee may overcome the data redundancies existing in the repeated use of information which are inappropriate and may provide tradeoff between coverage and precision. Improvements: Our proposed dynamic recommender system surmount the grey sheep problem and new user ramp up problem in many traditional recommender systems. Our proposed dynamic recommender system was compared with Ant clustering approach. The Experimental results shows that our approach offers better quality in terms of coverage, precision and F1 Measure than the traditional Ant clustering approaches. Applications: The recommender system is very effective in increasing the utility of e-commerce by minimizing the user surfing time and overload in servers.


Recommender Systems, Software Agents, Stimulated Annealing, Swarm Intelligence, User Profiles, Web Usage Mining.

Full Text:

 |  (PDF views: 215)


  • Kim S-S, You Y-Y, Kim S-H, Lee SK. Research direction of constructive e-business consulting for SMEs and Medium-Sized Enterprises (SMEs): Focusing on e-commerce business. Indian Journal of Science and Technology. 2015 Apr; 8(S7). DOI: 10.17485/ijst/2015/v8iS7/71290.
  • Nasraoui O, Krishnapuram R, Frigui H, Joshi A. Extracting web user profiles using relational competitive fuzzy clustering. International Journal of Artificial Intelligence Tools. 2000; 9(4):509–26.
  • Saka E, Nasraoui O. Improvements in flock-based collaborative clustering algorithms. Computational Intelligence, Collaboration, Fusion and Emergence. 2009:639–72 4. Wooldridge M. An introduction to multi agent systems. Wiley; 2002.
  • Weiss G. Multi agent systems: A modern approach to distributed artificial intelligence. MIT Press; 2103.
  • Aljumah A, Kouchay SA. Global ranking, web visibility and accessibility of quranic websites - An evaluation study-2015. Indian Journal of Science and Technology 2015 Nov; 8(30). DOI: 10.17485/ijst/2015/v8i1/76715.
  • Balabanovic M. An adaptive web page recommendation service. First International Conference on Autonomous Agents, New York; 1997. p. 378–85.
  • Herlocker JL, Konstan JA, Riedl J. Explaining collaborative filtering recommendations. ACM Conference on Computer Supported Cooperative Work, Philadelphia; 2000. p. 241–50.
  • Saka E, Nasraoui O. A recommender system based on the collaborative behavior of bird flocks. CollaborateCom; 2010. p. 1–10.
  • Gayathri S, Metilda MM, Babu SS. A shared nearest neighbour density based clustering approach on a proclus method to cluster high dimensional data. Indian Journal of Science and Technology. 2015 Sep; 8(22). DOI: 10.17485/ijst/2015/v8i22/79131.
  • Labroche N, Monmarche N, Venturini G. Antclust: Ant Clustering and web usage mining. Proceedings of GECCO; 2003. p. 25–36.
  • Kennedy J, Eberhart R. Particle swarm optimization. IEEE International Conference on Neural Networks. 1995; 4:1942–8.
  • Rathipriya R, Thangavel K, Bagyamani J. Binary particle swarm optimization based bi-clustering of web usage data. International Journal of Computer Applications. 2011; 25(2).
  • Dai L, Wang W, Shu W. An efficient web usage mining approach using chaos optimization and particle swarm optimization algorithm based on optimal feedback model. Mathematical Problems in Engineering; 2013.
  • Ester M, Kriegel HP, Wimmer M, XiaoweiXu. Incremental clustering for mining in a data warehousing environment. 24th International Conference on Very Large Data Bases; 1998. p. 323–33.
  • Benabdeslem K, Bennani. An incremental SOM for web navigation patterns clustering. 26th International Conference on Information Technology Interfaces; 2004. p. 209–13.
  • Cooley R, Mobasher B, Srivastava J. Web mining: Information and pattern discovery on the world wide web. Ninth IEEE International Conference Tools with AI (ICTAI ’97); 1997. p. 558–67.
  • Chen Z, Meng QC. An incremental clustering algorithm based on swarm intelligence theory. International Conference on Machine Learning and Cybernetics; 2004. p. 1768–72.
  • Cooley R, Mobasher B, Srivastava J. Web mining: Information and pattern discovery on the world wide web. Ninth IEEE International Conference Tools with AI (ICTAI ’97); 1997. p. 558–67.
  • Nasraoui O, Krishnapuram R, Joshi A. Mining web access logs using a relational clustering algorithm based on a robust estimator. Eighth International World Wide Web Conference (WWW ’99); 1999. p. 40–41.
  • Web Server Log files. Karuya University, Coimbatore, Tamil Nadu, India.
  • Available from:
  • Ciar RR, Bonto LS, Bayer MHP, Rabajante JF, Lubag SP, Fajardo AC, Cervanvia CR. Foraging behavior of singles bees TeragonulabiroiFriese: Distance, direction and height of preferred food source, Cornell University library; 2013.
  • Fisher D. Knowledge acquisition via incremental conceptual clustering. Machine Learning. 1987; 2(2):139–72.
  • Murphy RR. Introduction to AI robotics. The MIT Press; 2000.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.