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Intent Disentanglement and Feature Self-supervision for Novel Recommendation
[article]
2021
arXiv
pre-print
One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of
arXiv:2106.14388v1
fatcat:m4i73l45fzgzfhp7k3ud4aqc5y