# MVIN: Learning Multiview Items for Recommendation

arXiv: https://arxiv.org/abs/2005.12516 · HTML: https://mlsanity.com/paper/2005.12516/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 7 of 7)

- [Knowledge Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2205.00976/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Dynamic Knowledge Selector and Evaluator for recommendation with Knowledge Graph](https://mlsanity.com/paper/2502.15623/) (2025)
- [On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation](https://mlsanity.com/paper/2309.13384/) (2023)
- [Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods](https://mlsanity.com/paper/2505.16466/) (2025)
- [Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.08499/) (2026)

## Abstract (arXiv)

Researchers have begun to utilize heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model which provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. In addition, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.