Abstract

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 simpler 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 systems domain. The data model is based on the classical tab separated value data model with additional fields for encoding relational 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.

@inproceedings{SLTL:AnExtendedDataModelFormatForCompositeRecommendation,
author = {Alan Said and Babak Loni and Roberto Turrin and Andreas Lommatzsch},
title = {News Visualization based on Semantic Knowledge},
booktitle = {Proceedings of the 8th RecSys conference 2014},
year = {2014},
location = {Foster City, Silicon Valley, CA, USA},
numpages = {2}
}