Reviews: Modeling Dynamic Missingness of Implicit Feedback for Recommendation

This paper presents H4MF model (HMM MF for dynamic Missingness) for implicit feedback data. With implicit data, we only observe positive feedback and the missing entries (zeros) in the data can indicate either negative feedback or users are not exposed of the items. H4MF is based on the previous work on modeling user latent exposure (ExpoMF, Liang et al., Modeling user exposure in recommendation, 2016) -- the basic idea is that for each user-item pair, there is a latent binary variable to represent exposure; if it's 1, it means this user is exposed to the item thus 0 feedback mean true negative, while if it's 0, it means this user have not yet been exposed to this item yet. The difference in H4MF is that H4MF uses a hidden Markov model to capture the temporal dynamics in the user exposure (user intent in this paper). The basic idea is that whether or not a user is exposed to something can be dependent on some other items he/she has been exposed before.