Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems
Daniel Valcarce, Javier Parapar, Álvaro Barreiro · 2016
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 recommendation. 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.