# Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

arXiv: https://arxiv.org/abs/1909.06627 · HTML: https://mlsanity.com/paper/1909.06627/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 27 of 27)

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- [Accurate Portraits of Scientific Resources and Knowledge Service Components](https://mlsanity.com/paper/2204.04883/) (2022)
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- [Research on Intellectual Property Resource Profile and Evolution Law](https://mlsanity.com/paper/2204.06221/) (2022)
- [Research on Domain Information Mining and Theme Evolution of Scientific Papers](https://mlsanity.com/paper/2204.08476/) (2022)
- [Profiling and Evolution of Intellectual Property](https://mlsanity.com/paper/2204.09333/) (2022)
- [Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon](https://mlsanity.com/paper/2206.02160/) (2022)
- [A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT](https://mlsanity.com/paper/2206.02389/) (2022)
- [Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting](https://mlsanity.com/paper/2206.03128/) (2022)
- [Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa](https://mlsanity.com/paper/2207.02424/) (2022)
- [Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property](https://mlsanity.com/paper/2210.05947/) (2022)
- [Context-aware explainable recommendations over knowledge graphs](https://mlsanity.com/paper/2310.16141/) (2023)

## Abstract (arXiv)

Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between users and items, although some recently extended models utilize some auxiliary information to learn a unified latent factor for users and items. The unified latent factor only represents the characteristics of users and the properties of items from the aspect of purchase history. However, the characteristics of users and the properties of items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. Moreover, the latent factor models usually use the shallow projection, which cannot capture the characteristics of users and items well. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling the rich object properties and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items respectively through different meta-paths, and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are fused for the top-N recommendation. Moreover, to fuse information from different aspects more effectively, we further propose NeuACF++ to fuse aspect-level latent factors with self-attention mechanism. Extensive experiments on three real world datasets show that NeuACF and NeuACF++ significantly outperform both existing latent factor models and recent neural network models.