Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation

318 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Recommender Systems (2012, background): “Approaches to this problem include direct enhancement of the recommendation list’s diversity [36–38] and the use of hybrid recommendation methods [39].”
Calibrated recommendations (2018, background): “, [4, 15, 31, 32], including determinantal point processes [8, 11], or submodular optimization, e.”
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (2017, background): “For any item in Cu, the relevance score is the aggregated similarity to all items in Pu [22].”
Diversity in recommender systems - A survey (2017, background): “Hur-ley and Zhang [52] also modelled the trade-off between diversity and accuracy as a binary optimization problem.”
Recommender systems survey (2013, background): “To evaluate these aspects, various metrics have been proposed to measure recommendation novelty and diversity [105,220].”