Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

Li, Sheng; Kawale, Jaya; Fu, Yun

doi:10.1145/2806416.2806527

Cited by 361 publications

(221 citation statements)

References 25 publications

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“…2) and deep autoencoeder is that the later has multiple layers of encoders and decoders. However, there is no significant gain in going deeper as was shown in [23,24]. In recent years, with the success of deep learning in almost all areas of applied machine learning, such techniques have been leveraged for collaborative filtering as well; see for instance [23 -26].…”

Section: Fig 2 Autoencodermentioning

confidence: 99%

Deep latent factor model for collaborative filtering

et al. 2020

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Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all stateof-the-art collaborative filtering techniques.

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Section: Fig 2 Autoencodermentioning

confidence: 99%

Deep latent factor model for collaborative filtering

et al. 2020

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“…However, these models are typically simple neural network models and do not incorporate any content information. Recent work integrating deep learning with collaborative filtering mostly focuses on extracting content features from single modality such as texts [35][36][37] or images [38][39][40].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modality Deep Network for Cold-Start Recommendation

Sun

Zhang

2018

BDCC

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Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.

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“…We rst obtain the high-level feature representations from CAE, and then integrated them into the AutoSVD and AutoSVD++ model. An alternative optimization approach, which optimizes CAE and AutoSVD (AutoSVD++) simultaneously, could also apply [6]. However, the later approach need to recompute all the item content feature vectors when a new item comes, while in the sequential situation, item feature representations only need to be computed once and stored for reuse.…”

Section: Optimizationmentioning

confidence: 99%

“…• mSDA-CF [6] , mSDA-CF is a model that combines PMF with marginalized denoising stacked auto-encoders.…”

Section: Overall Comparisonmentioning

confidence: 99%

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AutoSVD++

Zhang

Yao

2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

111

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Collaborative ltering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based ltering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-cra ed feature engineering, which is usually noise-prone and biased by di erent feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational e ciency, which jointly models content information as representations of e ectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.

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Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

Cited by 361 publications

References 25 publications

Deep latent factor model for collaborative filtering

Deep latent factor model for collaborative filtering

A Multi-Modality Deep Network for Cold-Start Recommendation

AutoSVD++

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