Hybrid User Preference Models for Second Life and OpenSimulator Virtual Worlds

  • Joshua Eno
  • Gregory Stafford
  • Susan Gauch
  • Craig W. Thompson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


Virtual world user models have similarities with hypertext system user models. User knowledge and preferences may be derived from the locations users visit or recommend. The models can represent topics of interest for the user based on the subject or content of visited locations, and corresponding location models can enable matching between users and locations. However, virtual worlds also present challenges and opportunities that differ from hypertext worlds. Content collection for a cross-world search and recommendation service may be more difficult in virtual worlds, and there is less text available for analysis. In some cases, though, extra information is available to add to user and content profiles enhance the matching ability of the system. In this paper, we present a content collection system for Second Life and OpenSimulator virtual worlds, as well as user and location models derived from the collected content. The models incorporate text, social proximity, and metadata attributes to create hybrid user models for representing user interests and preferences. The models are evaluated based on their ability to match content popularity and observed user behavior.


Content Models Social Models Virtual Worlds Personalization Recommendations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    La, C.-A., Michiardi, P.: User Mobility in Second Life. In: 2008 ACM Workshop on Online Social Networks. ACM, Seattle (2008)Google Scholar
  2. 2.
    Varvello, M., Voelker, G.M.: Second Life: A Social Network of Humans and Bots. In: 20th International Workship on Network and Operating Systems Support for Digital Audio and Video. ACM, Amsterdam (2010)Google Scholar
  3. 3.
    OpenMetaverse Foundation,
  4. 4.
    Chirita, P.-A., Olmedilla, D., Nejdl, W.: PROS: A Personalized Ranking Platform for Web Search. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 34–43. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Joachims, T.: Optimizing Search Engines using Clickthrough Data. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York (2002)Google Scholar
  6. 6.
    Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web Search Based on User Profile Constructed without any Effort from Users. In: 13th International Conference on World Wide Web (WWW 2004). ACM, New York (2004)Google Scholar
  7. 7.
    Ashbrook, D., Starner, T.: Using GPS to LEarn Significant Locations and Predict Movement Across Multiple Users. J. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  8. 8.
    Loecher, M., Jebara, T.: CitySense:Multiscale Space Time Clustering of GPS Points and Trajectories. In: 2009 Joint Statistical Meeting (2009)Google Scholar
  9. 9.
    Sato, C., Takeuchi, S., Imbe, T., Ishibashi, S., Inami, M., Inakage, M., Okude, N.: TTI Model: Model Extracting Individual’s Curiosity Level in Urban Spaces. In: 8th ACM Conference on Designing Interactive Systems. ACM, New York (2010)Google Scholar
  10. 10.
    Bellotti, V., Begole, B., Chi, E.H., Ducheneaut, N., Fang, J., Isaacs, E., King, T., Newman, M., Partridge, K., Price, B., Rasmussen, P., Roberts, M., Schiano, D., Walendowski, A.: Activity-Based Serendipitous Recommnendations with the Magitti Mobile Leisure Guide. In: 26th SIGCHI Conference on Human Factors in Computing Systems. ACM, New York (2008)Google Scholar
  11. 11.
    Horvitz, E., Koch, P., Subramani, M.: Mobile Opportunistic Planning: Methods and Models. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 228–237. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Niu, W.T., Kay, J.: PERSONAF: Framework for Personalized Ontological Reasoning in Pervasive Computing. J. User Modeling and User-Adapted Interaction 20(1), 1–40 (2010)CrossRefGoogle Scholar
  13. 13.
    Eno, J., Gauch, S., Thompson, C.: Agent-Based Search and Retrieval in Virtual World Environments. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds.) Information Retrieval and Mining in Distributed Environments. SCI, vol. 324, pp. 125–143. Springer, Berlin (2011)CrossRefGoogle Scholar
  14. 14.
    Joachims, T.: Making Large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press, Cambridge (1999)Google Scholar
  15. 15.
    Meiss, M., Menczer, F., Fortunato, S., Flammini, A., Vespignani, A.: Ranking Web Sites with Real User Traffic. In: International Conferend on Web Search and Web Data Mining, pp. 65–76. ACM, Palo Alto (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Joshua Eno
    • 1
  • Gregory Stafford
    • 1
  • Susan Gauch
    • 1
  • Craig W. Thompson
    • 1
  1. 1.Computer Science and Computer Engineering DepartmentUniversity of ArkansasFayettevilleUSA

Personalised recommendations