  Faceted Search Systems (FSS) have become one of the main search interfaces
used in vertical search systems, offering users meaningful facets to refine
their search query and narrow down the results quickly to find the intended
search target. This work focuses on the problem of ranking type-based facets.
In a structured information space, type-based facets (t-facets) indicate the
category to which each object belongs. When they belong to a large multi-level
taxonomy, it is desirable to rank them separately before ranking other facet
groups. This helps the searcher in filtering the results according to their
type first. This also makes it easier to rank the rest of the facets once the
type of the intended search target is selected. Existing research employs the
same ranking methods for different facet groups. In this research, we propose a
two-step approach to personalize t-facet ranking. The first step assigns a
relevance score to each individual leaf-node t-facet. The score is generated
using probabilistic models and it reflects t-facet relevance to the query and
the user profile. In the second step, this score is used to re-order and select
the sub-tree to present to the user. We investigate the usefulness of the
proposed method to a Point Of Interest (POI) suggestion task. Our evaluation
aims at capturing the user effort required to fulfil her search needs by using
the ranked facets. The proposed approach achieved better results than other
existing personalized baselines.
