Crowdsourced Evaluation of Semantic Patterns for Recommendation

In this paper we explore the use of semantics to improve diversity in recommendations. We use semantic patterns extracted from Linked Data sources to surface new connections between items to provide diverse recommendations to the end users. We evaluate this methodology by adopting a bottom-up approach, i.e. we ask users of a crowdsourcing platform to choose a movie recommendation from among five options. We evaluate the results in terms of a diversity measure based on the semantic distance of topics and genres of the result list. The results of the experiment indicate that there are features of semantic patterns that can be used as an indicator of its suitability for the recommendation process.