DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference

He, Zhenwei; Zhang, Lei; Liu, Fangyi

doi:10.1007/s11633-020-1244-1

Cited by 10 publications

(5 citation statements)

References 53 publications

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“…In [28], it is shown that the convergence rate of the iterative equation in ( 6) can be improved to from if we replace in (5) with the following : (8) where…”

Section: L2( θJa) ≤ L2( θJa)mentioning

confidence: 99%

Multi-dimensional Classification via Selective Feature Augmentation

Jia

Zhang

2022

Mach. Intell. Res.

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In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features. In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features. Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension’s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard kNN, weighted kNN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

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“…In [28], it is shown that the convergence rate of the iterative equation in ( 6) can be improved to from if we replace in (5) with the following : (8) where…”

Section: L2( θJa) ≤ L2( θJa)mentioning

confidence: 99%

Multi-dimensional Classification via Selective Feature Augmentation

Jia

Zhang

2022

Mach. Intell. Res.

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“…Classical approaches use personality features to define a similarity score (proximity function) between users and use it (perhaps together with standard CF proximity) for recommendations [3,26,75] or add personality features to matrix factorization models in a way similar to SVD++ [15,24,25]. Approaches based on deep learning have only recently begun to incorporate personality features, and so far these approaches have not used standard personality types but rather inferred their own personality feature vectors [23,33,43]. In this work, we propose a hybrid approach that uses predicted MBTI personality type to inform a deep-learning-based recommender system.…”

Section: Related Workmentioning

confidence: 99%

Personality-Driven Social Multimedia Content Recommendation

Yang,

Nikolenko,

Huang

et al. 2022

Preprint

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Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. Still, McKinsey-style manual categorization is a very labour-intensive task that is probably impractical in a real-world scenario, so automated incorporation of audience behaviour and personality mining into industrial applications is necessary. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Even worse, there is no dataset available for the research community to serve as a benchmark and drive further research in this direction. The present study is one of the first attempts to bridge this important industrial gap, contributing not just a novel personality-driven content recommendation approach and dataset, but also facilitating a real-world ready solution which is scalable and sufficiently accurate to be applied in real-world settings. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable

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“…He et al studied Chinese painting modeling, designed a framework, and used it to test various current algorithms so that they could be applied to the classification and recognition of Chinese paintings [8]. She et al combined global and local features to classify western paintings and achieved quite satisfactory results [9].…”

Section: Related Workmentioning

confidence: 99%

Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification

Yang

Ye²,

Guo

2021

Complexity

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This paper presents an in-depth study and analysis of Chinese painting image classification by a multitask joint sparse representation algorithm for texture feature extraction of Chinese painting images and proposes a method to extract texture features directly for the original images. It simplifies the process of image grayscale conversion and preserves the information contained in the original Chinese painting images to the greatest extent. The algorithm uses the ideas of multicolor domain analysis and multiscale analysis, combined with the traditional grayscale coeval matrix to extract texture features. Experiments show that the multiscale grayscale cooccurrence matrix algorithm outperforms the traditional grayscale cooccurrence matrix algorithm and the color grayscale cooccurrence matrix algorithm. The discriminative ability of multiple features for target recognition is integrated by multitask learning, thus improving the robustness and generalization ability of the algorithm; meanwhile, the recognition accuracy is improved by using a two-level multitask learning mode to exclude the interference of a large number of irrelevant dictionary atoms. The experimental results show that the algorithm has higher recognition accuracy and better robustness than the existing sparse representation SAR target recognition algorithm. Configuration recognition experiments are conducted on different configurations of target data, and the experimental results show that the algorithm achieves better configuration recognition accuracy than existing algorithms.

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DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference

Cited by 10 publications

References 53 publications

Multi-dimensional Classification via Selective Feature Augmentation

Multi-dimensional Classification via Selective Feature Augmentation

Personality-Driven Social Multimedia Content Recommendation

Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification

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