Social movie recommender system based on deep autoencoder network using Twitter data

Tahmasebi, Hossein; Ravanmehr, Reza; Mohamadrezaei, Rezvan

doi:10.1007/s00521-020-05085-1

Cited by 74 publications

(34 citation statements)

References 49 publications

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“…Even though existing commercial RSs [16] make use of MF-based models, current research heads toward deep NN models [42]. Deep NN models can capture subtler complex nonlinear relations existing in the CF RS datasets [21] and they also enable information fusion [14,31] from CF, contentbased [39], social [35,40], context-aware [33] and demographic data [4]. Deep learning architectures are determined by the type of information that they manage: convolutional neural networks (CNNs) [36,37], multilayer perceptrons (MLPs) [2,13], autoencoders [41,43], etc.…”

Section: Related Workmentioning

confidence: 99%

Deep learning approach to obtain collaborative filtering neighborhoods

Bobadilla

González-Prieto

Ortega

et al. 2021

Neural Comput & Applic

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In the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

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Section: Related Workmentioning

confidence: 99%

Deep learning approach to obtain collaborative filtering neighborhoods

Bobadilla

González-Prieto

Ortega

et al. 2021

Neural Comput & Applic

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“…However, the research did not focus on building more multimode DNNs, nor did it merge audio and video data related to movies. In [16], a hybrid social recommender system utilizing a deep autoencoder network is introduced. e proposed approach employs collaborative and content-based filtering, as well as users' social influence.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, there is a shortcoming that the error is small in the long-term forecast, and the performance in the short-term forecast is not ideal, with high errors. A forecasting framework was established to predict the opening prices of stocks [16]. ey processed stock data through a wavelet transform and used an attention-based LSTM neural network to predict the stock opening price, with excellent results.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Feng

Liu

2020

Complexity

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To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.

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“…Collaborative filtering‐based recommender systems have a social property in terms of their nature. They try to predict the interest of the intended user through the interests of other users 6 …”

Section: Introductionmentioning

confidence: 99%

A social hybrid recommendation system using LSTM and CNN

Daneshvar

Ravanmehr

2022

Concurrency and Computation

Self Cite

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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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Social movie recommender system based on deep autoencoder network using Twitter data

Cited by 74 publications

References 49 publications

Deep learning approach to obtain collaborative filtering neighborhoods

Deep learning approach to obtain collaborative filtering neighborhoods

Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

A social hybrid recommendation system using LSTM and CNN

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