Contributions

This paper has three primary research contributions:

1.

Analysis of the item-based prediction algorithms and identification of different ways to implement its subtasks.

2.

Formulation of a precomputed model of item similarity to increase the online scalability of item-based recommendations.

3.

An experimental comparison of the quality of several different item-based algorithms to the classic user-based (nearest neighbor) algorithms.