Informative missingness in recommender system

Haiyun Jin
Recommender systems have been extensively adopted in a variety of areas such as electronic commerce, social media platforms, and content generators for individualized prediction and recommendation. Data sparsity is one of the main challenges in this topic as usually only a very limited number of user-item interactions are observed, resulting in a large proportion of missing data. Since users' ratings to items may depend on underlying user-specific preferences or item-specific characteristics, the missing data pattern...
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