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Content-based recommendations using similarity distance measures with application in the tourism domain

Ziogas Ioannis-Panagiotis, Streviniotis Errikos, Papadakis Harris, Chalkiadakis Georgios

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Summary

In this paper, we explore the use of similarity distance measures for Content-based recommendations for touristic attractions. First, we study ways of deploying hierarchies of points of interests (POIs) and operate upon them with well-known similarity distance measures originating in the text analysis domain. Then, we progressively build three novel, hierarchy-free, similarity measures, and discuss their strengths and weaknesses. We end up with a measure, the Weighted Extended Jaccard Similarity (WEJS) that combines information regarding the user interests (in the form of user preference-related weights applied on the items’ features) and specific items’ characteristics (in the form of particular values for the items’ features). As such, the use of WEJS allows the provision of recommendations that are effectively personalized. Interestingly, though it is a hierarchy-free measure, it is able to recommend items based on others that would naturally appear close in a features-based POIs hierarchy; while at the same time it is able to capture similarities among items that would be distant to each other in any hierarchy built solely based on the POIs’ features. Our systematic experimental evaluation on a real-world dataset showcases the benefits and limitations of the various measures, and confirms the effectiveness of WEJS in offering “rich” and personalized recommendations.

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