  Recently, user-oriented auto-encoders (UAEs) have been widely used in
recommender systems to learn semantic representations of users based on their
historical ratings. However, since latent item variables are not modeled in
UAE, it is difficult to utilize the widely available item content information
when ratings are sparse. In addition, whenever new items arrive, we need to
wait for collecting rating data for these items and retrain the UAE from
scratch, which is inefficient in practice. Aiming to address the above two
problems simultaneously, we propose a mutually-regularized dual collaborative
variational auto-encoder (MD-CVAE) for recommendation. First, by replacing
randomly initialized last layer weights of the vanilla UAE with stacked latent
item embeddings, MD-CVAE integrates two heterogeneous information sources,
i.e., item content and user ratings, into the same principled variational
framework where the weights of UAE are regularized by item content such that
convergence to a non-optima due to data sparsity can be avoided. In addition,
the regularization is mutual in that user ratings can also help the dual item
content module learn more recommendation-oriented item content embeddings.
Finally, we propose a symmetric inference strategy for MD-CVAE where the first
layer weights of the UAE encoder are tied to the latent item embeddings of the
UAE decoder. Through this strategy, no retraining is required to recommend
newly introduced items. Empirical studies show the effectiveness of MD-CVAE in
both normal and cold-start scenarios. Codes are available at
https://github.com/yaochenzhu/MD-CVAE.
