# Item Recommendation with Variational Autoencoders and Heterogenous Priors

arXiv: https://arxiv.org/abs/1807.06651 · HTML: https://mlsanity.com/paper/1807.06651/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation](https://mlsanity.com/paper/2405.00981/) (2024)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems](https://mlsanity.com/paper/2511.22515/) (2025)
- [HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation](https://mlsanity.com/paper/2205.12042/) (2022)
- [On the Mechanisms of Collaborative Learning in VAE Recommenders](https://mlsanity.com/paper/2511.06781/) (2025)

## Abstract (arXiv)

In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to collaborative filtering with side information, for instance when ratings are combined with explicit text feedback from the user. Instead of using a user-agnostic standard Gaussian prior, we incorporate user-dependent priors in the latent VAE space to encode users' preferences as functions of the review text. Taking into account both the rating and the text information to represent users in this multimodal latent space is promising to improve recommendation quality. Our proposed model is shown to outperform the existing VAE models for collaborative filtering (up to 29.41% relative improvement in ranking metric) along with other baselines that incorporate both user ratings and text for item recommendation.