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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications—Data mining; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering Keywords cold-start; IPS; beacon; collaborative filtering; FourSquare

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Emanuel Lacic, Dominik Kowald, Matthias Traub, Granit Luzhnica, Jörg Simon, Elisabeth Lex
• Keywords: Control Systems, RECSYS 2015