Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

Khan, Euna Mehnaz; Mukta, Md. Saddam Hossain; Ali, Mohammed Eunus; Mahmud, Jalal

doi:10.1145/3338244

Cited by 24 publications

(10 citation statements)

References 48 publications

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Section: B Prediction Of Psychological Attributes From Social Media C...mentioning

confidence: 99%

“…A considerable number of studies [2], [22], [23] advocate the success of predicting personalities and values from social media content. Khan et al [22] predict users' preferred movie genre based on their personality traits and values derived from their tweets. Golbeck et al [23] perform a regression analysis between publicly available users' Facebook content and their Big5 personality traits.…”

Section: B Prediction Of Psychological Attributes From Social Media C...mentioning

confidence: 99%

“…This feature extraction can be represented as: y = f (x), where x represents the input face image, y represents the deep face descriptor (values range from 0 to 1), and f is the function performed by the CNN. DeepFace library has seven different features based on various emotions: angry, happy, neutral, sad, surprise, disgust, and fear, which can provide valuable insights to derive human self-efficacy [22] [48] [48]. Table 3 represents a brief description of different features of DeepFace library based on diverse categories of emotion.…”

Section: This Subsection Discusses the Process Of Extracting Features...mentioning

confidence: 99%

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Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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General Self-Efficacy (GSE) is a vital attribute of human psychology that describes one's belief about his own ability to succeed in specific situations. GSE is composed of cognitive, social, and behavioral skills of an individual. In this research, we first develop a GSE classification model by using Facebook content (i.e., profile photos and statuses). We collect data from a total of 435 Facebook users in an ethical data collection manner. Two hybrid machine learning methods are applied based on distinct feature extraction approaches: tool-based and deep learning-based. In our tool-based approach, we employ Linguistic Inquiry and Word Count (LIWC) and Bidirectional Encoder Representations from Transformers (BERT) for text and Mediapipe and DeepFace for image feature extraction. We apply Particle Swarm Optimization (PSO) for feature selection, resulting in a robust tabular dataset with high predictive performance for GSE scores. In the deep learning-based approach, we apply BERT and 1-dimensional convolutional neural network (1D-CNN) for text feature extraction, while UNet++ handles image segmentation, and VGG16 and ResNet-152 contribute image features, fused via Canonical Correlation Analysis (CCA). We also integrate a coattention model for image and text features. Traditional machine learning models, including Random Forest (RF), Xgboost, AdaBoost, and Stacking, are then trained on the feature set to predict GSE scores. This comprehensive model showcases a multifaceted approach to GSE prediction, combining tool-based and deep learning methodologies for enhanced accuracy and insights. Then, we develop a GSE prediction model by using the mentioned tool-based (i.e., LIWC, BERT, Mediapipe, and DeepFace) and deep learning-based feature extraction methods from both image and text datasets. The tool-based model achieves remarkable accuracy percentages of 85.80% (text), 91.06% (image), and an outstanding 93.25% for the hybrid model. The deep learning-based model exhibits competitive results, with accuracies of 64.80% (text), 73.06% (image), and 81.87% for the hybrid model.

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Section: B Prediction Of Psychological Attributes From Social Media C...mentioning

confidence: 99%

Section: B Prediction Of Psychological Attributes From Social Media C...mentioning

confidence: 99%

Section: This Subsection Discusses the Process Of Extracting Features...mentioning

confidence: 99%

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Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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“…Scientists in other fields have used this framework for predicting individuals' work role performance [25], analysing the sense of humour [26] and inferring user movie preferences [27] and choices and behaviours on social media and digital platforms [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Soundscapes of morality: Linking music preferences and moral values through lyrics and audio

Preniqi¹,

Kalimeri²,

Saitis³

2023

Preprint

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Music is a fundamental element in every culture, serving as a universal means of expressing our emotions, feelings, and beliefs. This work investigates the link between our moral values and musical choices through lyrics and audio analyses. We align the psychometric scores of 1,480 participants to acoustics and lyrics features obtained from the top 5 songs of their preferred music artists from Facebook Page Likes. We employ a variety of lyric text processing techniques, including lexicon-based approaches and BERT-based embeddings, to identify each song's narrative, moral valence, attitude, and emotions. In addition, we extract both low- and high-level audio features to comprehend the encoded information in participants' musical choices and improve the moral inferences. We propose a Machine Learning approach and assess the predictive power of lyrical and acoustic features separately and in a multimodal framework for predicting moral values. Results indicate that lyrics and audio features from the artists people like inform us about their morality. Though the most predictive features vary per moral value, the models that utilised a combination of lyrics and audio characteristics were the most successful in predicting moral values, outperforming the models that only used basic features such as user demographics, the popularity of the artists, and the number of likes per user.Audio features boosted the accuracy in the prediction of empathy and equality compared to textual features, while the opposite happened for hierarchy and tradition, where higher prediction scores were driven by lyrical features. This demonstrates the importance of both lyrics and audio features in capturing moral values.The insights gained from our study have a broad range of potential uses, including customising the music experience to meet individual needs, music rehabilitation, or even effective communication campaign crafting.

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“…Table VIII summarizes the works that have used these datasets. [34], [95], [166], [87], [100], [148], [161], [106] N/A MovieLens [167], [40], [85], [96], [140], [32], [149], [31], [93] Movies Newsfullness [33], [81], [105] News articles Twitter [145], [161], [114], [116], [115] Friendships IMDB [139], [28], [145] Movies Last.fm [87], [148], [151], [153] Music Other datasets allrecipes.com [168], Amazon [169], Steam [163], [86] ADS [54], [56], Douban [80], [143], [170], IAPS [36], Facebook [58], , PsychoFlickr [55],…”

Section: Newsfullness Datasetmentioning

confidence: 99%

A Survey on Personality-Aware Recommendation Systems

Dhelim,

Aung,

Bouras

et al. 2021

Preprint

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With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personalityaware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

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Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

Cited by 24 publications

References 48 publications

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Soundscapes of morality: Linking music preferences and moral values through lyrics and audio

A Survey on Personality-Aware Recommendation Systems

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