Time-varying Item Feature Conditional Variational Autoencoder for Collaborative Filtering

The factors impacting on what users purchase can be classified as an internal factor which is time-invariant user's unique taste and an external factor which is time-varying item characteristic. However, the current recommendation system has a limitation of making recommendations based only on the user's history without taking into account the item features trends of the time, which prevents precise recommendations. In this paper, the recommendation system that reflects inter-items trends of time-based bin is proposed. We focus on creating a hybrid recommender system that could effectively combine time-varying content data with the users history data. Specifically, we use Conditional Variational Autoencoder (VAE) to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. In detail, the distributed representation per time bin can be extracted using LSTM. By applying a condition into VAE, a hybrid recommendation system can be created to reflect the item time-varying features. The proposed model in this paper differs from current studies in that it reflects the changing characteristics inherent of the products and utilizes it for recommendation. The Movielens 1M data and Amazon women's clothing dataset are used for the evaluation of the proposed model.