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A route recommendation system can provide better recommendation if it alsotakes collected user reviews into account, e.g. places that generally getpositive reviews may be preferred. However, to classify sentiment, manyclassification algorithms existing today suffer in handling small data itemssuch as short written reviews. In this paper we propose a model for a stronglyrelevant route recommendation system that is based on an MDL-based (MinimumDescription Length) sentiment classification and show that such a system iscapable of handling small data items (short user reviews). Another highlight ofthe model is the inclusion of a set of boosting factors in the relevancecalculation to improve the relevance in any recommendation system thatimplements the model.

Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors