# MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

arXiv: https://arxiv.org/abs/2210.07443 · HTML: https://mlsanity.com/paper/2210.07443/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2022.

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

- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation](https://mlsanity.com/paper/2508.04571/) (2025)
- [Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models](https://mlsanity.com/paper/2512.13980/) (2025)
- [DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking](https://mlsanity.com/paper/2404.04818/) (2024)
- [Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis](https://mlsanity.com/paper/2411.14463/) (2024)
- [From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation](https://mlsanity.com/paper/2511.19176/) (2025)

## Abstract (arXiv)

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals.