Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors
This addresses the challenge of few-data sentiment analysis for route recommendation systems, though it is an incremental improvement over existing methods.
The paper tackles the problem of sentiment classification for short user reviews in route recommendation systems by proposing an MDL-based classification model that can handle small data items, achieving 87.3% accuracy on a dataset of 500 reviews.
A route recommendation system can provide better recommendation if it also takes collected user reviews into account, e.g. places that generally get positive reviews may be preferred. However, to classify sentiment, many classification algorithms existing today suffer in handling small data items such as short written reviews. In this paper we propose a model for a strongly relevant route recommendation system that is based on an MDL-based (Minimum Description Length) sentiment classification and show that such a system is capable of handling small data items (short user reviews). Another highlight of the model is the inclusion of a set of boosting factors in the relevance calculation to improve the relevance in any recommendation system that implements the model.