Deep Transfer Collaborative Filtering for Recommender Systems

Gai, Sibo; Zhao, Feng; Kang, Yachen; Chen, Zhengyu; Wang, Donglin; Tang, Ao

doi:10.1007/978-3-030-29894-4_42

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…A generic deep transfer collaborative filtering (DTCF) architecture has been proposed by Gai et al 42 The authors integrate the collective matrix factorization and deep transfer learning to cope with both the ratings' statistic characteristics and the side information to generate effective latent representations. DCTF employs an individual Stacked Denoising AutoEncoder (SDAE) for users or items in each domain to integrate the side information and learn effective latent representations.…”

Section: Deep Neural Network Approachesmentioning

confidence: 99%

A social hybrid recommendation system using LSTM and CNN

Daneshvar

Ravanmehr

2022

Concurrency and Computation

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With the ever-increasing use of Internet and social networks that generate a vast amount of information, there is a serious need for recommendation systems. In this article, we propose a recommender system utilizing deep neural networks that simultaneously considers both the users' ratings to the movies and the visual features of the movie poster and trailer. For this purpose, a hybrid movie recommender system, RSLC-Net, has been developed using CNN and LSTM architectures. The proposed system considers the dynamics of users' interests in the collaborative filtering engine using the LSTM network that receives user-rating sequences. In the content-based filtering engine, utilizing CNN, the visual features of movie posters and trailers are extracted, and along with the actors and the directors, similar movies are recommended to the user. Moreover, each user's social influence is calculated employing the social information available on the user's Twitter account and used in the average movie rating to improve the effectiveness of the content-based filtering part. The required datasets have been collected from MovieTweetings, Mise-en-scène, and OMDB. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved in terms of MAE and RMSE compared to the best available methods.

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Section: Deep Neural Network Approachesmentioning

confidence: 99%

A social hybrid recommendation system using LSTM and CNN

Daneshvar

Ravanmehr

2022

Concurrency and Computation

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“…The fundamental assumption of CF is based on the similarities of users, which build a neighborhood group. Therefore, this technique is called user-based collaborative filtering [159,179,184,185]. In collaborative filtering, automatic predictions are made based on the reviews given by other people.…”

Section: Collaborative Filtering (Cf) Techniquementioning

confidence: 99%

“…Additionally, that is how the rest of the selections would be filled in. Although the CF technique is critical and has some issues, such as data sparseness and the cold-start problem, recommendation systems based on CF techniques have successfully worked for many renowned business stores and services [179,184,187,188]. Yu et al proposed a collaborative clothing recommendation system that overcomes the problem of capturing the aesthetic preferences of users by using a novel tensor factorization model [159].…”

Section: Collaborative Filtering (Cf) Techniquementioning

confidence: 99%

“…However, single cluster categorization is often problematic for prediction or recommendation as the user may have a variation of tastes with the different items [42,192]. Therefore, most of the model-based CF algorithms categorize the user into multiple clusters [184]. With the evolution of the use of learning algorithms, model-based recommendation systems have begun to use some algorithms such as association rules, clustering, decision tree, artificial neural network, link analysis, regression and Bayesian classifiers [57,193,194].…”

Section: Model-based Collaborative Filtering Techniquementioning

confidence: 99%

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Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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“…With the data explosion in recent years, recommendations are becoming increasingly attractive. Traditional single-criterion recommendation typically operates on twodimensional (2D) user-item ratings (Gai et al 2019;Xiao, Liang, and Meng 2019a). In single-criterion recommendation, there are two primary categories of algorithms: contentbased methods and collaborative filtering (CF) based methods, where matrix factorization is effective in learning effective latent factors for users and items (Xiao, Liang, and Meng 2019b;).…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems

Chen

Wang

2021

AAAI

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Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings. To mitigate this issue, we consider effectively incorporating the side information and cross-domain knowledge in tensor decomposition. A deep transfer tensor decomposition (DTTD) method is proposed by integrating deep structure and Tucker decomposition, where an orthogonal constrained stacked denoising autoencoder (OC-SDAE) is proposed for alleviating the scale variation in learning effective latent representation, and the side information is incorporated as a compensation for tensor sparsity. Tucker decomposition generates private users and items' latent factors to connect with OC-SDAEs and creates a common core tensor to bridge different domains. A cross-domain alignment algorithm (CDAA) is proposed to solve the rotation issue between two core tensors in source and target domain. To the best of our knowledge, this is the first work in Tucker decomposition based recommendations to use deep structure to incorporate the side information and cross-domain knowledge. Experiments show that DTTD outperforms state-of-the-art related works.

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Deep Transfer Collaborative Filtering for Recommender Systems

Cited by 14 publications

References 20 publications

A social hybrid recommendation system using LSTM and CNN

A social hybrid recommendation system using LSTM and CNN

Fashion Recommendation Systems, Models and Methods: A Review

Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems

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