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Item-Based Vs User-Based Collaborative Recommendation Predictions

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10546)

Abstract

The use of personalised recommendation systems to push interesting items to users has become a necessity in the digital world that contains overwhelming amounts of information. One of the most effective ways to achieve this is by considering the opinions of other similar users – i.e. through collaborative techniques. In this paper, we compare the performance of item-based and user-based recommendation algorithms as well as propose an ensemble that combines both systems. We investigate the effect of applying LSA, as well as varying the neighbourhood size on the different algorithms. Finally, we experiment with the inclusion of content-type information in our recommender systems. We find that the most effective system is the ensemble system that uses LSA.

Keywords

Neighborhood Size Content Type Information Ensemble System Item-based Recommendation Algorithms Collaborative Recommender Systems 
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 International Publishing AG 2018

Authors and Affiliations

  1. 1.University of MaltaMsidaMalta

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