Abstract
We present here a new technique for making predictions on recommender systems based on collaborative filtering. The underlying idea is based on selecting a different number of neighbors for each user, instead of, as it is usually made, selecting always a constant number k of neighbors. In this way, we have improved significantly the accuracy of the recommender systems.
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Notes
- 1.
Indeed, \(\sqrt{\mathit{MSD}}\) fulfills the definition of distance given in metric spaces when ∀x ∈ U\(\forall i \in I\ v(x,i)\neq \bullet \).
- 2.
In metric spaces, the distance d(x, y) must fulfill that d(x, x) = 0. However, as may be seen, ρ(x, x) = cos(x, x) = 1.
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Hernando, A., Bobadilla, J., Serradilla, F. (2010). Collaborative Filtering Based on Choosing a Different Number of Neighbors for Each User. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_15
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