On the Value of Random Opinions in Decentralized Recommendation

  • Elth Ogston
  • Arno Bakker
  • Maarten van Steen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4025)


As the amount of information available to users continues to grow, filtering wanted items from unwanted ones becomes a dominant task. To this end, various collaborative-filtering techniques have been developed in which the ratings of items by other users form the basis for recommending items that could be of interest for a specific person. These techniques are based on the assumption that having ratings from similar users improves the quality of recommendation. For decentralized systems, such as peer-to-peer networks, it is generally impossible to get ratings from all users. For this reason, research has focused on finding the best set of peers for recommending items for a specific person. In this paper, we analyze to what extent the selection of such a set influences the quality of recommendation. Our findings are based on an extensive experimental evaluation of the MovieLens data set applied to recommending movies. We find that, in general, a random selection of peers gives surprisingly good recommendations in comparison to very similar peers that must be discovered using expensive search techniques. Our study suggests that simple decentralized recommendation techniques can do sufficiently well in comparison to these expensive solutions.


Prediction Function Collaborative Filter Mean Absolute Error Similar User Recommendation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Tech. Rep. MSR-TR-98-12, Microsoft Research, Redmond, WA, USA (May 1998)Google Scholar
  2. 2.
    Herlocker, J., Konstan, J., Riedl, J.: An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  3. 3.
    Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  4. 4.
    Miller, B., Konstan, J., Riedl, J.: PocketLens: Toward a Personal Recommender System. ACM Transcations on Information Systems 22(3), 437–476 (2004)CrossRefGoogle Scholar
  5. 5.
    Oregon State University. COllaborative Filtering Engine version 0.4 (September 2005),
  6. 6.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. Chapel Hill, NC, United States (1994)Google Scholar
  7. 7.
    Resnick, P., Varian, H.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  8. 8.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proceedings 10th International Conference on the World Wide Web (WWW10), Hong Kong, May 2001, pp. 285–295 (2001)Google Scholar
  9. 9.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings 1995 ACM SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, USA, May 1995, pp. 210–217 (1995)Google Scholar
  10. 10.
    University of Minnesota. GroupLens Home Page (September 2005),
  11. 11.
    Voulgaris, S., van Steen, M.: Epidemic-style Management of Semantic Overlays for Content-Based Searching. In: Proceedings 11th International Euro-Par Conference, Lisbon, Portugal, August 2005, pp. 1143–1152 (2005)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Elth Ogston
    • 1
  • Arno Bakker
    • 1
  • Maarten van Steen
    • 1
  1. 1.Department of Computer ScienceVrije UniversiteitAmsterdamThe Netherlands

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