A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems

@article{Xu2021AUC,
  title={A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems},
  author={Yuanbo Xu and En Wang and Yongjian Yang and Yi Chang},
  journal={ArXiv},
  year={2021},
  volume={abs/2205.09670}
}
Existing neural-network based recommender systemsusually first employ matrix embedding (ME)as a pre-process to learn usersand itemsrepresentations (latent vectors)to make accurate Top-k recommendations. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions, while ignoring the underlying relatedness between users and items, which is an ideological drawback. On the other hand, ME models directlyemploy inner products as a default loss function… 
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References

SHOWING 1-10 OF 51 REFERENCES
Neural Graph Collaborative Filtering
TLDR
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Effective metric learning with co-occurrence embedding for collaborative recommendations
Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation
TLDR
A novel latent factor model called adaptive deep latentfactor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration, and a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings is proposed.
NAIS: Neural Attentive Item Similarity Model for Recommendation
TLDR
This work proposes a neural network model named Neural Attentive Item Similarity model (NAIS), which is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
Convolutional Matrix Factorization for Document Context-Aware Recommendation
TLDR
A novel context-aware recommendation model that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF) that captures contextual information of documents and further enhances the rating prediction accuracy is proposed.
FISM: factored item similarity models for top-N recommender systems
TLDR
An item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices using a structural equation modeling approach.
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
TLDR
This paper revisits GCN based CF models from two aspects and proposes a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user- item interaction data.
Neural Collaborative Filtering
TLDR
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Heterogeneous Information Network Embedding for Recommendation
TLDR
A novel heterogeneous network embedding based approach for HIN based recommendation, called HERec is proposed, which shows the capability of the HERec model for the cold-start problem, and reveals that the transformed embedding information from HINs can improve the recommendation performance.
Deep social collaborative filtering
TLDR
DSCF is proposed, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems and shows the effectiveness of the proposed framework on two-real world datasets.
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