424 citations · 855 across the 27 of their papers we have counts for
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cs.IR2018★ 1 cited
Deriving item features relevance from collaborative domain knowledge
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based f…
cs.IR2018
Eigenvalue analogy for confidence estimation in item-based recommender systems
Maurizio Ferrari Dacrema, Paolo Cremonesi
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of…