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

TL;DR

This paper introduces a route recommendation system that leverages MDL-based sentiment classification and relevance boosting factors to effectively handle small data, such as short user reviews, improving recommendation quality.

Contribution

The paper presents a novel MDL-based sentiment classification model combined with relevance boosting factors for improved route recommendations with limited data.

Findings

Effective handling of short reviews with MDL-based sentiment classification

Inclusion of boosting factors enhances relevance in recommendations

System outperforms traditional models on small data sets

Abstract

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.