Social and opinion data improve location recommendations online
SOAR: Social and Opinion-Aware Recommendations
Machine LearningInformation Retrieval
Summary
Online platforms often try to recommend places users might like, but this can be hard because people have different habits and changing tastes. The paper proposes SOAR, a model that uses both a user's friends and the feelings expressed in reviews to better guess which places a user might enjoy. They tested it on real-world data and found that including written reviews about places made recommendations more accurate. This approach helps tailor suggestions more personally for each user.
What this means in practice
- •For mobile app developers: Improve location-based recommendation features by integrating social connections and review sentiment analysis for users.
- •For digital marketing teams: Enhance targeted advertising by using social and opinion data to better match user preferences with promoted locations.
Authors
Giannis Christoforidis, Apostolos N. Papadopoulos
Abstract
One of the enduring challenges in online platforms is recommendation. Both communities and advertisers face difficulties in analyzing complex user behavior, identifying habits and evolving preferences, and providing timely recommendations that are relevant to user interests. In particular, point of interest recommendation poses the additional challenge of identifying the factors that influence user behavior, capturing user tendencies, and generating accurate, personalized recommendations for each individual. In this work, we propose SOAR, a Social and Opinion Aware Recommendation model that incorporates both the user's friendship network and textual reviews to assess whether a location is suitable for a specific user, with a particular emphasis on the sentiment of shared reviews. Experimental results in real-world datasets demonstrate that incorporating reviews into the recommendation process improves the precision of the recommendation.