A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

Duong, Tan Nghia; Than, Viet Duc; Vuong, Tuan Anh; Tran, Trong Hiep; Dang, Quang Hieu; Nguyen, Duc Minh; Pham, Manh Hung

doi:10.1007/978-3-030-29029-0_30

Cited by 9 publications

(13 citation statements)

References 17 publications

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“…In this paper, we have three main contributions. Firstly, when investigating the genome scores information used in our previous article [44], we found that the total number of tags can be reduced by combining similar tags together while still getting competitive results. Secondly, we could even compress this information further by a method in deep learning called autoencoder to automatically learn the hidden representation of the genome scores.…”

Section: Proposed Modelmentioning

confidence: 98%

“…In [44], we noticed that similarity measures using the rating information faces some problems. Firstly, in practice the rating matrix is highly sparse (for example, 99.47% of the ratings in the MovieLens 20M dataset are missing); therefore, evaluating the relevance between two movies that have many ratings but share only few common users using above similarity measures is not reliable.…”

Section: Previous Workmentioning

confidence: 99%

“…Obviously, content-related information of a movie cannot be described exactly using such largely distributed values. This affects negatively the accuracy of evaluating the analogy between two movies using genome scores as in [44].…”

Section: A Clustering Relevant Genome Tagsmentioning

confidence: 99%

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Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

et al. 2020

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While collaborative filtering (CF) is the most popular approach for recommendation systems, it only makes use of the ratings given to items by users and neglects side information about user attributes or item features. In this work, a natural language processing (NLP) technique is applied to generate a more consistent version of Tag Genome, a side information which is associated with each movie in the MovieLens 20M dataset. Subsequently, we propose a 3-layer autoencoder to create a more compact representation of these tags which improves the performance of the system both in accuracy and in computational complexity. Finally, the proposed representation and the well-known matrix factorization techniques are combined into a unified framework that outperforms the state-of-the-art models by at least 2.87% and 3.36% in terms of RMSE and MAE, respectively.

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Section: Proposed Modelmentioning

confidence: 98%

Section: Previous Workmentioning

confidence: 99%

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Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

et al. 2020

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“…In [38], a number of problems regarding similarity measurement method using the rating information were noticed. Firstly, the rating matrix in practice is extremely sparse (for example, 99.47% entries of this matrix in the MovieLens 20 M dataset are missing), which makes it hard to evaluate the relevance between two items that have many ratings but only share a few common users.…”

Section: Previous Workmentioning

confidence: 99%

“…• NMF: optimization procedure is a regularized SGD based on Euclidean distance error function [41] with regularization strength of 0.02 and 40 hidden factors. • kNN-Content [38]: PCC genome is used as the similarity measure. • kNN-Content AE -SVD and kNN-Content AE -SVD++ [39]: 600-feature vectors for movies are learned from 1044 NLP-preprocessed genome tags using a 3-layer AE.…”

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confidence: 99%

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

et al. 2022

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Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

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A content-based recommender system with consideration of repeat purchase behavior

Kuo

Cheng

2022

Applied Soft Computing

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A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

Cited by 9 publications

References 17 publications

Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

A content-based recommender system with consideration of repeat purchase behavior

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