User and item profile expansion for dealing with cold start problem

Bahrani, Payam; Minaei‐Bidgoli, Behrouz; Parvïn, Hamïd; Mirzarezaee, Mitra; Keshavarz, Ahmad; Alinejad‐Rokny, Hamid

doi:10.3233/jifs-191225

Cited by 20 publications

(9 citation statements)

References 30 publications

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“…Several researchers have attempted to address the cold start problem; however, they were largely unsuccessful. Some studies suggest demographic data as a solution to this problem, while others suggest personality [39][40][41]. Each of them plays a critical role in determining the interests of users, as the users' personality represents a rich and explicit source of prediction of interests, as is the case with demographic data.…”

Section: Methodsmentioning

confidence: 99%

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The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

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Section: Methodsmentioning

confidence: 99%

“…As shown in Table 12, comparing the proposed system against different previous systems, the results show that incorporating the personality traits, demographic data and interested topics improved the proposed system. Semantic similarity [39] Yes -- [40] Yes Personality - [35] No -- [42] No -- [36] No Personality Semantic similarity [30] Yes…”

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confidence: 99%

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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“…To diagnose illnesses from skin dermatological images, the feature extractor techniques such as LBP, HOG, SURF, and neural network-based features extracts hybrid features. Numerous machine learning and computer vision applications use data fusion [19,[42][43][44]. The challenge of combining many feature vectors, known as features fusion, is critical.…”

Section: Feature Extractionmentioning

confidence: 99%

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Rahman¹,

Nasir²,

A-Alam³

et al. 2022

Preprint

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Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

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“…In recent years, Machine Learning/Deep Learning techniques have been widely used to identify patterns in complex data such as clinical imaging, genomics, bioimaging, and phenotypic data [9][10][11][12][13]. Machine learning algorithms for example, NN, SVM, KNN, Decision Tree, displayed promising ability in prediction and classification [14][15][16][17][18][19][20][21][22][23] including estimation of bone age [24,25]. The medical and biological datasets are increasing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

Sharifonnasabi¹,

Jhanjhi²,

Jacob³

et al. 2022

Preprint

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Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

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User and item profile expansion for dealing with cold start problem

Cited by 20 publications

References 30 publications

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

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