GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation

  title={GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation},
  author={Zahra Zamanzadeh Darban and Mohammad Hadi Valipour},
  journal={Expert Syst. Appl.},

Graph Network based Approaches for Multi-modal Movie Recommendation System

Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single- modal counterparts.

Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings

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Adam: A Method for Stochastic Optimization

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Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs

This paper introduces a neural network architecture which computes a non-linear matrix factorization from sparse rating inputs and provides an implementation of the algorithm as a reusable plugin for Torch, a popular neural network framework.

Facial Expression Analysis under Partial Occlusion

This survey provides a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems and outlines existing challenges in overcoming partial occlusions.

Autoencoder-Based Collaborative Filtering

This research proposed an autoencoder based collaborative filtering method, in which pretraining and stacking mechanism is provided, which has shown its potential and effectiveness in getting higher recall.

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Hybrid Recommender System based on Autoencoders

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