Movie Recommendation Based on Visual Features of Trailers

Fan, Yaning; Wang, Ying; Yu, Haitao; Liu, Baoling

doi:10.1007/978-3-319-61542-4_23

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…GoogleNet (Inception-v1) is a deep convolutional neural network architecture developed by Google researchers in 2014 for image categorization. GoogleNet's usage of the Inception module, which enables efficient use of computing resources while retaining high accuracy, is one of its defining characteristics [40]. Figure 7 is a detailed illustration of the inception module.…”

Section: Googlenetmentioning

confidence: 99%

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

Kanwal¹,

Nawaz²,

Mustafa³

et al. 2023

Preprint

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Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

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Section: Googlenetmentioning

confidence: 99%

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

Kanwal¹,

Nawaz²,

Mustafa³

et al. 2023

Preprint

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“…Zhao et al [19] presented movie recommendation model using visual features extracted from picture data. Moreover, Fan et al [34] extracted visual features from trailers [35] directly using Youtube-8M dataset [36]. Compared to all of the above forms of contexts, there are relatively few of works considering the fusion of multiple features (such as textual features and visual features) to recommender systems.…”

Section: B Context-aware Recommendationsmentioning

confidence: 99%

VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

Wang

Chen

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items. In particular, the visual information of items, such as the movie poster, can be considered as the supplement for item description documents, which helps to obtain more item features. In this paper, we focus on movie recommender system and propose a probabilistic matrix factorization based recommendation scheme called visual recurrent convolutional matrix factorization (VRConvMF), which utilizes the textual and multi-level visual features extracted from the descriptive texts and posters respectively. We implement the proposed VRConvMF and conduct extensive experiments on three commonly used real world datasets to validate its effectiveness. The experimental results illustrate that the proposed VRConvMF outperforms the existing schemes.

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Uncovering Significance of Feature-Selection in Recommender System Models

Pahuja¹,

Chaudhari²

2019

2019 Global Conference for Advancement in Technology (GCAT)

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Movie Recommendation Based on Visual Features of Trailers

Cited by 6 publications

References 14 publications

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

Uncovering Significance of Feature-Selection in Recommender System Models

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