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The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.