  Online forums enable users to discuss together around various topics. One of
the serious problems of these environments is high volume of discussions and
thus information overload problem. Unfortunately without considering the users
interests, traditional Information Retrieval (IR) techniques are not able to
solve the problem. Therefore, employment of a Recommender System (RS) that
could suggest favorite's topics of users according to their tastes could
increases the dynamism of forum and prevent the users from duplicate posts. In
addition, consideration of semantics can be useful for increasing the
performance of IR based RS. Our goal is study of impact of ontology and data
mining techniques on improving of content-based RS. For this purpose, at first,
three type of ontologies will be constructed from the domain corpus with
utilization of text mining, Natural Language Processing (NLP) and Wordnet and
then they will be used as an input in two kind of RS: one, fully ontology-based
and one with enriching the user profile vector with ontology in vector space
model (VSM) (proposed method). Afterward the results will be compared with the
simple VSM based RS. Given results show that the proposed RS presents the
highest performance.
