algorithm [18]. Using the obtained parameters, in the second (prediction) step, the
overall rating of a given unknown item is predicted as the most likely value (i.e.,
the rating value with the highest probability). This approach has been extended to
multi-criteria ratings, and the detailed algorithm can be found in [79].
Sahoo et al. [79] also compare their model in Fig. 24.2b with the model that assumes independence among multi-criteria ratings conditional on the latent variables,
and found that the model with dependency structure performs better than the one
with the independence assumption. This finding demonstrates the existence of the
“halo effect” in multi-criteria rating systems. The “halo effect” is a phenomenon often studied in psychometric literature, which indicates a cognitive bias whereby the
perception of a particular object in one category influences the perception in other
categories [94]. In multi-criteria recommender systems, the individual criterion ratings provided by users are correlated due to the “halo effect”, and particularly more
correlated to an overall rating than to other individual ratings [79]. In other words,
the overall rating given by the user to a specific item seems to affect how the user
rates the other (individual) criteria of this item. Thus, controlling for an overall rating reduces this halo effect and helps to make individual ratings independent of each
other, as represented in the chow-Liu tree dependency structure (Fig. 24.2b).