Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of ...

Particularly, in this paper, we use a hidden Markov model (HMM) to represent the dynamic missing- ness of implicit feedback and the estimated missingness of ...

Thus the key to modeling the dynamic missingness is how to utilize the temporal information of implicit feedback to capture the asymmetric item dependencies.

To model and exploit the dynamics of missingness, we propose a latent variable named "user intent" to govern the temporal changes of item missingness, and a ...

Dec 7, 2021 · To model and exploit the dynamics of missingness, we propose a latent variable named “user intent ” to govern the temporal changes of item ...

This paper proposes using user intents in a dynamic model to address implicit data that is missing not at random for recommendation system problems.

We proposed a framework that seamlessly combines HMM and MF to model the dynamic missing mechanism of implicit feedback for sequential recommendation.

To model and exploit the dynamics of missingness, we propose a latent variable named "user intent" to govern the temporal changes of item missingness, and a ...

This work proposes a latent variable named "user intent" to govern the temporal changes of item missingness, and a hidden Markov model to represent such a ...

Implicit feedback is widely used in collaborative filtering methods for sequential recommendation. It is well known that implicit feedback contains a large ...