Precedence Mining in Group Recommender Systems

  • Venkateswara Rao Kagita
  • Vineet Padmanabhan
  • Arun K. Pujari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


We extend the Precedence mining model for personal recommendation as outlined in Parameswaran, [6] in three different ways. Firstly, we show how precedence mining model can be used for recommending items of interest to a group of users (group recommendation) and compare and contrast our model with traditional group recommendation models like collaborative and Hybrid. Secondly, we extend the precedence mining model to incorporate ratings for items and experimental results show that the goodness of recommendation is improved. The third extension is related to the issue of new items being ignored which is a fundamental problem plaguing collaborative and precedence mining algorithms. When recommendations are based on other users interests (like in Collaborative recommender systems) the possibility of not recommending a new item which has not been consumed by many of the users is high though the new item may be of interest to the target user. We outline two models, Vector precedence-mining and Hybrid precedence-mining that addresses this issue.


Precedence Probability Recommender System Recommendation Algorithm Traditional Group Group Recommender 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Venkateswara Rao Kagita
    • 1
  • Vineet Padmanabhan
    • 1
  • Arun K. Pujari
    • 1
  1. 1.School of Computer and Information SciencesUniversity of HyderabadIndia

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