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.
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Azzopardi, J. (2018). Item-Based Vs User-Based Collaborative Recommendation Predictions. In: Szymański, J., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2017. Lecture Notes in Computer Science(), vol 10546. Springer, Cham. https://doi.org/10.1007/978-3-319-74497-1_16
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DOI: https://doi.org/10.1007/978-3-319-74497-1_16
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