Venue Suggestion Using Social-Centric Scores

TL;DR

This paper introduces social-centric relevance scores for personalized venue suggestions, demonstrating that social information from location-based social networks improves suggestion accuracy over content-based methods.

Contribution

The paper proposes a novel set of social-centric relevance scores for user modeling in venue recommendation systems, emphasizing social information from location-based social networks.

Findings

Social scores outperform content-based scores in venue suggestion accuracy.

Experiments conducted on TREC dataset validate the effectiveness of social-centric scores.

Social information enhances personalization in venue recommendations.

Abstract

User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.