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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 indoi:10.1145/2934732.2934737 dblp:conf/ceri/ValcarcePB16 fatcat:ppfv7vpknzduxa76r5o3ofzkly