Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors

  • Olfa Nasraoui
  • Mrudula Pavuluri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3932)


We present a Context Ultra-Sensitive Approach based on two-step Recommender systems (CUSA-2-step-Rec). Our approach relies on a committee of profile-specific neural networks. This approach provides recommendations that are accurate and fast to train because only the URLs relevant to a specific profile are used to define the architecture of each network. Similar to the task of completing the missing pieces of a puzzle, each neural network is trained to predict the missing URLs of several complete ground-truth sessions from a given profile, given as input several incomplete subsessions. We compare the proposed approach with collaborative filtering showing that our approach achieves higher coverage and precision while being faster, and requiring lower main memory at recommendation time. While most recommenders are inherently context sensitive, our approach is context ultra-sensitive because a different recommendation model is designed for each profile separately.


Recommender System Output Node User Profile Session Length Recommendation Strategy 
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 2006

Authors and Affiliations

  • Olfa Nasraoui
    • 1
  • Mrudula Pavuluri
    • 2
  1. 1.Dept. of Computer Science and Engineering Speed Scientific SchoolUniversity of Louisville LouisvilleUSA
  2. 2.Dept. of Electrical and Computer EngineeringThe University of Memphis MemphisUSA

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