Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems

Daniel Valcarce, Javier Parapar, Álvaro Barreiro
2016 Proceedings of the 4th Spanish Conference on Information Retrieval - CERI '16  
The use of Relevance-Based Language Models for top-N recommendation has become a promising line of research. Previous works have used collection-based smoothing methods for this task. However, a recent analysis on RM1 (an estimation of Relevance-Based Language Models) in document retrieval showed that this type of smoothing methods demote the IDF effect in pseudo-relevance feedback. In this paper, we claim that the IDF effect from retrieval is closely related to the concept of novelty in
more » ... ndation. We perform an axiomatic analysis of the IDF effect on RM2 concluding that this kind of smoothing methods also demotes the IDF effect in recommendation. By axiomatic analysis, we find that a collection-agnostic method, Additive smoothing, does not demote this property. Our experiments confirm that this alternative improves the accuracy, novelty and diversity figures of the recommendations.
doi:10.1145/2934732.2934737 dblp:conf/ceri/ValcarcePB16 fatcat:ppfv7vpknzduxa76r5o3ofzkly