# Motif Enhanced Recommendation over Heterogeneous Information Network

arXiv: https://arxiv.org/abs/1908.09701 · HTML: https://mlsanity.com/paper/1908.09701/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs](https://mlsanity.com/paper/2210.13710/) (2022)
- [Motif Graph Neural Network](https://mlsanity.com/paper/2112.14900/) (2021)
- [Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users](https://mlsanity.com/paper/2204.11849/) (2022)

## Abstract (arXiv)

Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and items. However, existing meta-path based methods only consider first-order relations, ignoring higher-order relations among the nodes of textitsame type, captured by textitmotifs. In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations. With MEMP-based similarities between users and items, we design a recommending model MoHINRec, and experimental results on two real-world datasets, Epinions and CiaoDVD, demonstrate its superiority over existing HIN-based RS methods.