User Click Research Articles

A CTR Prediction Model With Double Matrix-Level Cross-Features

CTR prediction is an important task in recommender systems, which is used to estimate the likelihood of a user clicking on an advertisement.In the past, the CTR prediction model based on the deep neural network mainly obtains the implicit feature combination of the model at the bit-wise level, and the interpretability and generalization of the model are poor. At the same time, the prediction accuracy of the model is poor. For the above problems, We propose a click-through rate prediction model (DTM) with double matrix-level cross-features. The model integrates various components such as multi-head self-attention, residual network and interaction network into an end-to-end model, and automatically obtains explicit feature combinations at the vector-wise level and bit-wise level, which not only has better interpretability, generalization and memory, and reduce the inherent flaws and engineering complexity of multi-modules. The experimental results show that on the datasets Criteo and Avazu, compared with other state-of-the-art CTR prediction models, the AUC values of the DTM model are increased by 4% and 3% on average, and the loss values are decreased by 3.5% and 2.8% on average, respectively.

Clustering-based Factorization Machines for Advertisement Click prediction

Advertisements establish the reputation of an organization and promote its business growth. The advancements in technology have replaced traditional promotions with personalized advertising techniques. Advertisement click prediction utilizes machine learning and deep learning techniques to predict the likelihood of a user clicking on an advertisement. These predictions require intensive computational power to handle large sparse datasets and train complex models that effectively capture feature interactions. In this paper, an efficient method is presented to identify the representative points of the large dataset and capture higher-order interactions of the click features by utilizing K-Means clustering and FNFM (Field Aware Neural Factorization Machine) classifiers, respectively. Our proposed model is trained on 2 lakh representative data points drawn from 1 million samples and is tested with various test sample sizes of 1,2,3,4 million. To show the efficiency of the proposed model, we have evaluated the area under the curve (AUC) results and compared them with the Base models from the literature. Hence, by addressing the above-mentioned factors it is evident that the proposed clustering-based FM model is yielding better results.

Deep context interaction network based on attention mechanism for click-through rate prediction

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliary ads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction.

How Headline Strategies and News Sources Affect Number of User Clicks on WeChat

ABSTRACT This study used 2,623 WeChat article headlines on the Chengdu school food safety event and employed quantitative content analysis to explore the dynamic relationship of headline strategy (i.e., headline style and headline frame) and news source with user clicks at distinct phases of the issue–attention cycle. The results first revealed that various types of headline styles affect the number of user clicks based on the issue–attention phase, but no single type dominates public attention at any phase. Second, reader attention could be attracted using the information with the singleton detection mode. However, the feature search mode could filter official-oriented discourse. Third, the “treatment recommendation” headline frame, which clearly summarises the core of texts, generally lacked executability and had a poor effect on audience mobilisation. Fourth, readers depended on certain media channels under low-choice media environment, and information issued by administrative sources was not widely spread because of the limitation on their functions and specific targets. Finally, news from traditional mainstream media, online communities, and independent sources were more click-attractive news than the news from other sources, thus demonstrating a complementary relationship between official reports and social media discourses.

A product recommendation system for solving the cold start problem

The recommendation of product or service based on application needs different product attributes and user requirements to be analyzed. But if both the kinds of information are unavailable then such kind of issue in the recommendation system is known as the clod start problem.[1] In this presented work, the effort is made in order to understand and resolve the issue of recommendation system. Thus, a four phase recommendation system is introduced in this work. In first phase, the web usage data is preprocessed which is used for further recommendations of products. In next phase, the frequent pattern based recommendation is performed. In third phase, the recommendation is made on the basis of user current search (user click streams) and similar user behaviors available in web access logs. Additionally, the final recommendation is made on the basis of filtering the results obtained in third phase and the cost, brand and social review of the product or service. The implementation of the system is performed on JAVA technology and the results in terms of accuracy, error rate is computed. The results show the prediction is accurate and required less computational resources. Thus, in recommendation system design the model is acceptable for use and future extension of work for solving the issue of cold start problem.

A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks

People connected by huge-size socialnetworks are highly dependent on recommendation systems to discover interesting persons, contents, and commodities. Extracting user interests and modeling their evolutions from user historical behavior are vital for algorithms to judge whether users are interested in given persons and items. The existing methods have two shortcomings: firstly, they do not have the high-order ability to process temporal sequences, which leads to their inability to mine the evolution of user interests; secondly, the models take full large-scale information of items as input, which causes a severe problem of overfitting. Thus, we propose a new hybrid neural network, DGRU, which integrates Factorization-Machine Based Neural Network (DeepFM) and Gated Recurrent Unit Neural Network (GRU). The DeepFM component is responsible for performing the autonomic feature combination, and the GRU component is designed to model user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors. It contains information on what users like and dislike. Moreover, the conciseness of the format is helpful to avoid the problem of overfitting. Experiments on three real datasets demonstrated that the proposed model has a better CTR prediction performance and robustness than the state-of-the-art models.

Applicability analysis of prediction methods in the system for selection personalized offers by analytical modeling

The relevance of the work is justified by the frequent occurrence of the need to solve the problems of choosing personalized offers in information systems and the many possible methods of machine learning, among which it is necessary to choose the most suitable one. The purpose of this study is to simulate a system for selecting personalized offers as a queuing system for estimating equipment costs when using each of the methods necessary to service the required part of requests for a given time limit. This solves the problem of assessing the minimum number of servicing devices (backend servers) required to ensure the operation of the system at a given level. The paper shows that the system can be described by a multichannel queuing system without losses. The distribution function of the spent time of the request in the system (the service time plus the waiting time in the queue) is calculated, since in the literature for such systems only the distribution function of the waiting time in the queue is described. Transformations of the expression for the probability of waiting are given, which solve the overflow problem in the software implementation. In the final part, as an example, the system was modeled according to the given parameters, and the minimum number of servicing devices was estimated to ensure a given system response time. Based on the data obtained, it is possible to make a decision on the advisability of using one or another method for predicting the frequency of user clicks or ranking.

From Noon to Sunset: Interactive Rendering, Relighting, and Recolouring of Landscape Photographs by Modifying Solar Position

AbstractImage editing is a commonly studied problem in computer graphics. Despite the presence of many advanced editing tools, there is no satisfactory solution to controllably update the position of the sun using a single image. This problem is made complicated by the presence of clouds, complex landscapes, and the atmospheric effects that must be accounted for. In this paper, we tackle this problem starting with only a single photograph. With the user clicking on the initial position of the sun, our algorithm performs several estimation and segmentation processes for finding the horizon, scene depth, clouds, and the sky line. After this initial process, the user can make both fine‐ and large‐scale changes on the position of the sun: it can be set beneath the mountains or moved behind the clouds practically turning a midday photograph into a sunset (or vice versa). We leverage a precomputed atmospheric scattering algorithm to make all of these changes not only realistic but also in real‐time. We demonstrate our results using both clear and cloudy skies, showing how to add, remove, and relight clouds, all the while allowing for advanced effects such as scattering, shadows, light shafts, and lens flares.

Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches

Nowadays, the banking industry has moved from traditional branch services into mobile banking applications or apps. Using customer segmentation, banks can obtain more insights and better understand their customers' lifestyle and their behavior. In this work, we described a method to classify mobile app user click behavior into two groups, i.e. SME and Non-SME users. This task enabled the bank to identify anonymous users and offer them the right services and products. We extracted hand-crafted features from click log data and evaluated them with the Extreme Gradient Boosting algorithm (XGBoost). We also converted these logs into images, which captured temporal information. These image representations reduced the need for feature engineering, were easier to visualize and trained with a Convolutional Neural Network (CNN). We used ResNet-18 with the image dataset and achieved 71.69% accuracy on average, which outperformed XGBoost, which only achieved 61.70% accuracy. We also evaluated a semi-supervised learning model with our converted image data. Our semi-supervised method achieved 73.12% accuracy, using just half of the labeled images, combined with unlabeled images. Our method showed that these converted images were able to train with a semi-supervised algorithm that performed better than CNN with fewer labeled images. Our work also led to a better understanding of mobile banking user behavior and a novel way of developing a customer segmentation classifier.

Quad division prototype selection-based k-nearest neighbor classifier for click fraud detection from highly skewed user click dataset

In online advertising, the user-clicks dataset based fraudulent publishers’ classification models exhibit poor performance due to high skewness in class distribution of the publishers. The nearest-neighbor based classification techniques are popularly used to reduce the impact of class skewness on performance. The Nearest-Neighbor techniques use Prototype Selection (PS) methods to select promising samples before classifying them for reducing the size of training data. Although Nearest-Neighbor techniques are simple to use and reduce the negative impact of the loss of potential information, they suffer from higher storage requirements and slower classification speed when applied on datasets with skewed class distributions. In this paper, we propose a Quad Division Prototype Selection-based k-Nearest Neighbor classifier (QDPSKNN) by introducing quad division method for handling uneven class distribution. The quad-division divides the data into four quartiles (groups) and performs controlled under-sampling for balancing class distribution. It reduces the size of the training dataset by selecting only the relevant prototypes in the form of nearest-neighbors. The performance of QDPSKNN is evaluated on Fraud Detection in Mobile Advertising (FDMA) user-click dataset and fifteen other benchmark imbalanced datasets to test its generalizing behaviour. The performance is also compared with one baseline model (k-NN) and four other prototype selection methods such as NearMiss-1, NearMiss-2, NearMiss-3, and Condensed Nearest-Neighbor. The results show improved classification performance with QDPSKNN in terms of precision, recall, f-measure, g-mean, reduction rate and execution time, compared to existing prototype selection methods in the classification of fraudulent publishers as well as on other benchmark imbalanced datasets. Wilcoxon signed ranked test is conducted to demonstrate significant differences amid QDPSKNN and state-of-the-art methods.

Going to Extremes: Weakly Supervised Medical Image Segmentation

Medical image annotation is a major hurdle for developing precise and robust machine-learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points using the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. Through experimentation on several medical imaging datasets, we show that the predictions of the network can be refined using several rounds of training with the prediction from the same weakly annotated data. Further improvements are shown using the clicked points within a custom-designed loss and attention mechanism. Our approach has the potential to speed up the process of generating new training datasets for the development of new machine-learning and deep-learning-based models for, but not exclusively, medical image analysis.

Invention a Paradigm to Discovery the Network Navigation Using Poisson Distribution

Due to increasing the act of Applied science College in Tamil Nadu, the level of competition for admission price is also increased. By implementing some dynamic strategies only the academic introduction s can meet their own competition. One survey clearly commonwealth that more than 75% of the Engineering Colleges their forcefulness is less than thirty % of their actual intake. Hence the surveillance is the job for the insane asylum s. One more survey shows that every year 10% of the applied science college’ windup their affiliation and blessing due to lack of admittance, and 5% of the engineering college have decided to sell due to lack of strength. With the strong effort and dynamic strategy framed by the institution, the nominee finds admission in an institution only when their own orientation matches exactly, otherwise the candidate continues to go by the next alternate in the list of preference [1]. This paper clearly emphasis some factors influenced to identify the pattern for getting the potency of the bookman to meet at least the breakeven point. In plus to the above, the Populace Wide Web in cyberspace plays an important role to store, part and distribute data about the academic innovation. A social survey states that more than 65% of the admissions gained by their effective network pages. The exponential ontogeny of the World Wide Web has provided an excessive prospect to study the potency student and their deportment by using www accession logs. If the institution’s web sphere clearly contains the information required for the potential educate, surely they can attract the above by which they can get more number of admissions even beyond our jurisdiction.[2] Some of the attractions from the potential students while accessing the web site for getting the admission are: get the required information by clicking minimum act of hits from the vane Page, no network traffic occurred while accessing and navigating the college World Wide Web website. Search interrogation will be rectified within a short period of answer time by implementing the practice of search railway locomotive optimization, search engine spiders. Always use fastest and latest browsers and operating systems in their WWW and not to display much more web server erroneousness while navigating the college web site. For attracting the counseling class and other state students, this network Thomas Nelson Page swordplay an important part. World Wide Web usage Mine lying is the practical application of data excavation techniques to very large data deposit to selection pattern radiation diagram. In general every World Wide Web server keeps a record book of all Synonyms/Hyponyms (Ordered by Estimated Frequency) of noun transaction needed for the potential students and act as a bridge between the potential students and introduction. The record contains full phase of the moon contingent about every user click to the entanglement documents of the entanglement site. The useful record 5 senses of detail needs to be scrutinized and inferred to gather knowledge about actual potential student and their parent preferences in accessing www pages. In recent years several method acting s have been proposed for mining web logarithm data. This theme its main intention is to use the statistical method of Poisson statistical distribution analysis to breakthrough out the higher probability session episode and also comparability the efficiency of our developed algorithm with Poisson value. The subject field of large volumes of click stream data demands the employment of data mining method. Conducting data mining on records of web host contains the determination of frequently occurring access sequences. A statistical method of toxicant distribution clearly shows the probability of oftenest of specific consequence when the norm probability of a single natural event is known. Here the probability of poison value is compared with the efficiency of our developed algorithm. For more bit of transactions, our developed algorithm its performance is better than poison value[3].Because our algorithms excerpt the authority tier as dependent rather independent. The Poisson distribution is used in this paper to find out the probability frequency of particular page is visited by the user as independent, but the result of the developed algorithm is dependent

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after user clicks. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.

Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.

A Hybrid Framework for Session Context Modeling

Understanding user intent is essential for various retrieval tasks. By leveraging contextual information within sessions, e.g., query history and user click behaviors, search systems can capture user intent more accurately and thus perform better. However, most existing systems only consider intra-session contexts and may suffer from the problem of lacking contextual information, because short search sessions account for a large proportion in practical scenarios. We believe that in these scenarios, considering more contexts, e.g., cross-session dependencies, may help alleviate the problem and contribute to better performance. Therefore, we propose a novel Hybrid framework for Session Context Modeling (HSCM), which realizes session-level multi-task learning based on the self-attention mechanism. To alleviate the problem of lacking contextual information within current sessions, HSCM exploits the cross-session contexts by sampling user interactions under similar search intents in the historical sessions and further aggregating them into the local contexts. Besides, application of the self-attention mechanism rather than RNN-based frameworks in modeling session-level sequences also helps (1) better capture interactions within sessions, (2) represent the session contexts in parallelization. Experimental results on two practical search datasets show that HSCM not only outperforms strong baseline solutions such as HiNT, CARS, and BERTserini in document ranking, but also performs significantly better than most existing query suggestion methods. According to the results in an additional experiment, we have also found that HSCM is superior to most ranking models in click prediction.

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning

ObjectivesTo develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.MethodsIn total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC).ResultsWe achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947.ConclusionsThe proposed joint system exhibited fair performance compared to segmentation only and classification only systems.Key Points• The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination.• The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems.• The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

Click Prediction for Advertisement in Websites using Linear Regression

Prediction is one of the most powerful and effective method used nowadays for improvement in business. Machine Learning Algorithms plays a vital role in predicting the future of business. It is widely used in the field of Marketing and Advertising fields also. The Commercial Value for the advertisement is gained based on the user click on the website. Digital advertisement and marketing play very important role in influencing the profit of business. Many Machine Learning algorithms were used for predicting and analyzing the online advertisement. In this paper, Linear Regression is used for predicting the user click on the advertisement.

Click-cut: a framework for interactive object selection

In order to simplify interactive image segmentation, We propose a new interactive segmentation framework for cutting an object from its background by which user interaction is reduced to only one click. Our proposed interactive segmentation framework consists of two steps: the image is first segmented automatically and then the object is extracted via user interaction, achieving image interactive segmentation. Combining the color and texture features of an image, We propose automatic partitioning for the image on the basis of modularity optimization. We construct the image region similarity network and partition the network into communities. We propose several region selection strategies. The user only needs to provide an interaction click. The region where the user click is merged with its adjacent regions recursively in accordance with the region selection strategy, resulting in user-desired regions. The image is finally divided into the foreground and the background. Compared with existing interactive segmentation approaches, the proposed method uses the simplest user interaction: it does not simultaneously require foreground or background markers for input. We evaluate our framework on different public image datasets. The experimental results indicate that the proposed method is superior to all existing interactive segmentation approaches. Results show that our framework achieves 67.5% accuracy on Grabcut, 80.8% accuracy on BSD_SSDS and 78.6% accuracy on MSRC_HighQuality.

Learning to Rank for Educational Search Engines

In this digital age, there is an abundance of online educational materials in public and proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to build keyword-based search engines over these collections. In modern Web search engines, high-quality rankings are obtained by applying machine learning techniques, known as learning to rank (LTR). In this article, our focus is on constructing machine-learned ranking models to be employed in a search engine in the education domain. Our contributions are threefold. First, we identify and analyze a rich set of features (including click-based and domain-specific ones) to be employed in educational search. LTR models trained on these features outperform various baselines based on ad-hoc retrieval functions and two neural models. As our second contribution, we utilize domain knowledge to build query-dependent ranking models specialized for certain courses or education levels. Our experiments reveal that query-dependent models outperform both the general ranking model and other baselines. Finally, given well-known importance of user clicks in LTR, our third contribution is for handling singleton queries without any click information. To this end, we propose a new strategy to “propagate” click information from the other, similar, queries to the singleton queries. The proposed click propagation approach yields a better ranking performance than the general ranking model and another baseline from the literature. Overall, these findings reveal that both the general and query-dependent ranking models, trained using LTR approaches, yield high effectiveness in educational search, which may ultimately lead to a better learning experience.

СУЧАСНИЙ ЗАГОЛОВОК НОВИН В УКРАЇНСЬКИХ ІНТЕРНЕТ-ЗМІ: МІЖ КЛІКБЕЙТОМ ТА ІНФОРМАТИВНІСТЮ

In modern Internet media, the function of searching for material by the words used in the title is added to its the nominal, informational, appellate, and advertising functions; often the headline is the only read element of the publication. News headlines create a kind of media picture of the world. Some of the headlines in the Internet media are purely informative, they exactly reproduce the content of the news. "Partially informative" headlines provide incomplete information and therefore may reflect distorted reality. Encouraging, intriguing headlines encourage reading. Rarely is there an intellectual variety of advertising and intriguing title, built on associations with images of literature, science, art, the use of phraseology, antonyms, paronyms and more. Such a title requires a certain level of knowledge from the recipient. The desire for sensationalism determine other trends that often go beyond journalistic standards. The title may not reflect the content of the journalistic work, distort the facts. Common in some online media are clickbaits - headlines that make the user click on them. They used manipulations with quantity; words with the possibility of double interpretation are used; negative information about the minority is provided, while positive information about the majority is indicated only in the text of the publication; the title takes the form of a question to which the publication gives a negative answer. The low linguistic and professional competence of the authors leads to distortion of the information in the titles. Today there are many reasons to consider the title as an completed work, because the recipient often reads only it. However, in modern headlines there is less and less literary and linguistic creativity, skillful use of vocabulary and phraseology. The informativeness of the title comes to the fore, but the information is often inaccurate, consciously or unconsciously distorted, which can lead to violations of ethical norms, distortions in the reproduction of reality.