An Extended Data Model Format for Composite Recommendation
Alan Said, Babak Loni, Roberto Turrin, Andreas Lommatzsch · 2014
Current de facto data model standards in the recommender systems field do not support easy encoding of heterogeneous data aspects such as context, content, social ties, etc. In order to facilitate a sim-pler means of sharing and using the rich datasets used by research-as well as production systems today, in this paper we propose a data model standard for heterogeneous datasets in the recommender sys-tems domain. The data model is based on the classical tab separated value (TSV) data model with additional fields for encoding rela-tional data in JSON format. Through using already established data sharing formats, we intend to make the usage of the data model as effortless as possible, i.e. there already exist generic tools for parsing and managing the data format in most programming languages. We invite the RecSys community to contribute to the proposed data model in order to increase ease of use and adoption.