Novelty and Diversity in Recommender Systems [chapter]

Pablo Castells, Neil J. Hurley, Saul Vargas
2015 Recommender Systems Handbook  
Ninguna cosa despierta tanto el bullicio del pueblo como la novedad. Francisco de Quevedo La unidad es la variedad, y la variedad en la unidad es la ley suprema del Universo Isaac Newton i Abstract Novelty and diversity as relevant dimensions of retrieval quality are receiving increasing attention in the Information Retrieval and Recommender Systems fields. Both problems have nonetheless been approached under different views and formulations in Information Retrieval and Recommender Systems
more » ... ctively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. We find considerable room for research towards the formalization of diversification methods, evaluation methodologies, and metrics. Furthermore, we ask ourselves whether there should be some natural connection between the perspectives on diversity in Information Retrieval and Recommender Systems, given that recommendation is after all an information retrieval problem. In the present work we propose an Information Retrieval approach to the evaluation and enhacement of novelty and diversity in Recommender Systems. We draw models and solutions from text retrieval and apply them to recommendation tasks in such a way that the recent advances achieved in the former can be leveraged for the latter. We also propose a new formalization and unification of the way novelty and diversity are evaluated on Recommender Systems, considering rank and relevance as additional and meaningful aspects for the evaluation of recommendation lists. We propose a framework that includes and unifies the main state of the art metrics for novelty and diversity in Recommender Systems, generalizing and extending them with further properties and flexibility in configuration. Our contributions are tested with standard Recommender Systems collections, in order to validate our proposals and provide further insights. iii
doi:10.1007/978-1-4899-7637-6_26 fatcat:53veooy4dzgzxcr3volnel46w4