  A recommender system is an information filtering technology which can be used
to predict preference ratings of items (products, services, movies, etc) and/or
to output a ranking of items that are likely to be of interest to the user.
Context-aware recommender systems (CARS) learn and predict the tastes and
preferences of users by incorporating available contextual information in the
recommendation process. One of the major challenges in context-aware
recommender systems research is the lack of automatic methods to obtain
contextual information for these systems. Considering this scenario, in this
paper, we propose to use contextual information from topic hierarchies of the
items (web pages) to improve the performance of context-aware recommender
systems. The topic hierarchies are constructed by an extension of the
LUPI-based Incremental Hierarchical Clustering method that considers three
types of information: traditional bag-of-words (technical information), and the
combination of named entities (privileged information I) with domain terms
(privileged information II). We evaluated the contextual information in four
context-aware recommender systems. Different weights were assigned to each type
of information. The empirical results demonstrated that topic hierarchies with
the combination of the two kinds of privileged information can provide better
recommendations.
