Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.

Lacic Emanuel, Kowald Dominik, Traub Matthias, Luzhnica Granit, Simon Jörg Peter, Lex Elisabeth

Veröffentlicht in: ACM

Veröffentlicht von: 9th ACM Conference on Recommender Systems

cold-start, IPS, beacon, collaborative filtering, FourSquare

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