Learning Context-Aware Ranking
In this chapter, we propose a learning method for the problem setting of contextaware ranking. This problem setting has been investigated in detail in the last chapter. We have seen, that a context-aware ranking 
can be modelled by a real-valued function 
. Now, we will show how this function can be optimized. The optimization will be done with respect to the pairwise training data d
s
, that is inferred from the sparse and incomplete observations s. The whole chapter assumes, that y can be expressed as a differentiable, non-recursive function with a finite set of parameters Θ. This assumption holds for many models, including the factorization models that we will introduce in the next chapter.
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Rendle, S. (2010). Learning Context-Aware Ranking. In: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16898-7_4
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DOI: https://doi.org/10.1007/978-3-642-16898-7_4
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