Tourism recommender systems have been an important area of research for over a decade and are gaining momentum with the help of ontology-driven systems. However, further improvements are warranted to enhance the relevance of the recommendations provided by such systems.

In this paper, the authors present a novel technique that uses a double recommendation method by using direct demand entry by the user as well as feedback from the user.

An ontology-based approach is used to disambiguate the user demand vocabulary along with user behavior modeling. The Jena framework is used to create the operational rules, with programming in ontology tools such as the resource description framework (RDF), RDF schema (RDFS), an ontological evaluation layer (OEL), and SPARQL.

Formulas used to calculate the fit between the user’s demand and available hotels are given. Results of the evaluation and validation conducted for the presented algorithms are given and compared with another method.

The contents of the paper provide an introduction to the research reported therein. However, it seems rather scanty in details, possibly due to a restriction on the number of pages. Nonetheless, this paper will be a good introduction to this area for anyone interested in tourism recommender systems.