Improving the Ranking Algorithm

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Topic: Improving the Ranking Algorithm of the App Evaluation Mechanism

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AbstractEvidence for communication with assessors

There are certain problems on the app evaluation mechanism of major app distribution platforms like the Apple App store and Android App Store. Based on the problems, I plan to improve the evaluation mechanism for the app store based on the analytics with the hierarchy process and sorting algorithm. The setting of standards with effectiveness of App evaluation algorithm that can be ranked easily. The improvement is based on the existing algorithm with verifying on the validity on the actual effect. The app stores are considered to be the one the popular ways for the content to the mobile device users today. The competing apps and the problems on the presenting of the developer apps are for the users with the non-trivial approach. It includes the investigation app store content organization through AppEco with the Artificial Life Model of Model app ecosystem. The developer agents build and then upload the apps for the app store with the user agents with the store and downloading the apps.

Table of Contents

Introduction

The app distribution platforms like the Apple App store and the ratings are for the software that is on the downloads. It is based on the frequency of the interaction with the app, numbers and the quality of ratings and comments. The keyword density and the relevance and uninstalls with the satisfied on the perspectives for user experience. It leads to the fact with the apps that is for the full of violence and pornography are still released. I plan to improve the app evaluation mechanism of App store based on the analytical hierarchy with sorting algorithm for comprehensive analysis rating method. The focus is on the pioneering iOS app store that is based on Android Market with the apps and then downloading it for every second. The mobile app includes the content of the users and the rapid increase of app store content for the users. There is a possibility to create the different app stores with modifying the app stores with handling the experimental tool to address the challenges. Hence, the simulation is based on the developers and downloaded by users with the features. The focus is on the app store with providing content to the users for encouraging the app downloads in terms for the download to browse ratio.

Background

The study includes the mobile app ecosystems with the related work with contextualizing and informing the current study. The related to the app ecosystem in the prediction of app downloads and usage. The inferring is for the number of downloads with the app based on the ranking for the Apple iOS App store with the enabling on the investors with estimating on the profits like the app for the specific rank. There is no certainty about the app with the appearance on the chart. The revealed interesting app usage behaviors are for the users to handle the average session with the app that is for the news applications (Jain et al., 2019). It is set for the informative approach with Evolutionary Computing and Agent Based Simulation where there are growing studies with effects for human interaction.

Literature Review (state-of-the-art)

The comprehensive analysis rating method is considered important method with the actual data for evaluating the objects. It includes the series of indicators for the ranking of certain characteristics which includes the weighted scoring method, efficiency coefficient methods and the membership function rating. The practices are for setting the standard weight with specific indicator that is for the rating goals (Vinod et al., 2019). Hence, the actual value of indicators is based on obtaining the weighted evaluation total score with the applications of the mathematical techniques. The Markov chains and the stochastic models are mainly applicable for the evaluation mechanism. There is a need to develop the risks assessment strategy with combination of risks analysis and the hierarchy process with the semi-quantitative analysis for the public health data. The App store app rating mechanism is based on the platform that is different and then there are rating mechanism for the App as well. The scoring algorithm tend to evaluate the different types of the subjects through analyzing and then calculating the existing data, and then focusing on the evaluation, assessment and proper management for the type of subject. The ranking is based on the evaluation with the series of things that involves the product connecting the users, goods, content and service. The larger amount of data includes scoring facilities for the unified assessment with the use of rating to quantify and visualize the multiple role performance for the internet. It is found to be conducive for the effective management roles with automatic assigning. Hence, the consumers need to focus on the ratings influences with the spending choices and ratings which are considered to be the source of data for the scoring systems with the commercialized companies (Tinschert et al., 2017).

The app platform needs to evaluate the lists for the different platform and certain standards where the legitimacy and the rationality tends to meet the requirements for the particular public release. Hence, the current observation is for the certain problems in the App evaluation mechanism that needs to focus on the violence and the pornography that is released with the omission of the other evaluation mechanism. The researchers need to focus on the adoption and then handling the entities with the artefacts to handle the media for communication. The interaction and imitation are to create the music which depends on the agent preferences. The indirect interaction involves the focus with the human culture that is for handling the development and consumption for the artefacts by the agents. Hence, the current internet product connects the users, goods, content and the service providers. Here, the subject is to focus on the multiple roles with the internet that is for the consumers to handle the rating systems through commercialized companies. The current observation and the problems in the App evaluation which are related to the violence and the pornography that is mainly due to the omission with the damage of the security for the application software market and the application platform that tends to lack the credibility as well (Lu et al., 2017). The focus is on the apps which is mainly full of violence and then there is a need to evaluate the mechanism for the App store that is based on the analytical hierarchy process and the sorting algorithm.


Hypothesis

The approach is based on the mobile app ecosystem with the coevolving systems of apps developers and the users forming the complex relationship. Hence, there are artefacts and the abstraction of the apps that are for the traditional agent-based models that comes through the explicit modelling of artefacts. The components of the AppEco are based on the apps, users and the app store developers (Xiong et al., 2017). There is probability to handle the building apps and then work on the models with the learning from downloads and also improve the best app. It comes through the app artefacts which is uploaded through the developer agent. The ranking purposes are for the app to keep the record for the total downloads and then receiving to date all the number of the downloads which are for the users. Hence, the developers are also unaware of the user preferences with the abstracted grid and model features.
Justification
The justified approach is about the user agent to keep the record for the apps with the number of days that are for handling the browsing of the app store next. This is for the users to record the number of friends with the influence that infects the value through the random number. It involves the management of the relationship with new social networking technologies. The primary function is to shop front for the users and then focusing on updating the app stores as well (Bohnen et al., 2016). This involves the app ranking algorithm that is configurable with the different ranking algorithm that is for the higher cumulative downloads. This includes the increased agent population with the developer agents in the ecosystem for the next timestep.

Methodology

The research is based on the investigation for the application software with the mechanism in market. Hence, there is a need to take Apple, Android and the Amazon platforms to analyze the ratings with the App legitimacy. The collection is based on the app users with the real time feedback. It is then compared to the evaluation mechanism that is based on the platform for judging the rationality for the different algorithms. The platform is defined for the comparison on the efficiency of the app rating for the different platforms and then consult the professional technicians as well. The improvement is made through reasonable evaluation algorithm with App software that involves the general software evaluation method. The APP evaluation is for solving the problems with attempts to propose the methods to filter the APP user comments and then extract the words that helps in describing about the user emotions. It comes through carrying out the filtered user comments effectively. The methodology is also on the improvement through the App user comments that are short and tendentious. The problems and the study need to focus on the methods for filter APP user comments with extracting the words for describing the different features and the user emotions. The APP evaluation is then carried out based on the filtered user comments (Schoappe et al., 2017).

Design


 The Google PageRank includes the companies with the ranking algorithms with the success that depends on the ranking mechanism. It includes the performance and the average packet delay which is for handling the improved fairness characteristics. The advancement is based on improving the web and link the building through site to the target site. The design is for the developer agents build and upload the apps. Not only this, there are updating on the app store with defining on the app ranking algorithm with the higher number of the cumulative downloads. Hence, the user agents browse and download the apps where the user tend to work on the keyword search and return the random number of the apps (Kao et al., 2017). Not only this, the increased agent population comes though investigating the content in the app store with the simulation to match and handle the content. There are positive feedback loops that are for maximizing the effectiveness as well. The number of the iOS users, apps and the downloads are for handling the sales with the simplicity for the calculation. The experimental setup includes the app ranking algorithm with the trigger of more downloads that comes from the users. Hence, this comes through appearing on the chart with increased downloads or even more. There are iOS and Android with the top app ranking algorithm to handle the weighted 4-day and 7-day downloads.

Hence, the understanding is based on the environmental standards with the applications where the studies are informative (Taylor et al., 2017). The Evolutionary Computing and Agent Based Simulation with the growing study that comes through emergent effects for the human interaction. The disappearance is mainly for understanding the urbanization with the models to study on the epidemic disease over the complex social networks. The involved locations include scaling for financial pricing that arises with interactions in between the market participants.

Evaluation

The evaluation is based on the training data with the number of judgements which are for measuring the contributions for relevance signals. The applications are defined for the learning to rank failure method with the baseline methods. It includes the judgements with the discriminative learning methods with target ranking metrics that includes performance for learning to rank methods which might be unpredictable till there are training sets are large (Mezgec et al., 2017). The application search is based on the largest commercial email and the loud file services of stage. It includes the results with efficient way for the user to properly examine the results. Hence, the result randomization includes quantifying position biasing with employing results and collecting the data of the user. Not only this, there are segmented bias model with the idea about the segments that comes with the email corpus. The labels feature and the training are for the query instance for the randomized data. The logistics regression models like the single position includes the feature vectors and the negative training examples. The learning to rank algorithm includes the model with the scoring function with adjustment of email category and user interaction (Genc et al., 2017). The ranking feature tends to seem with the small number of features with user interaction and using Multiple Additive Regression Trees learning algorithm. The regression model analysis includes the changes with the different lengths of query features. The learning to rank with selection comes with personal search. There is infeasibility for the existing click models with overcome the inherent selection biasing for the application. The methods are based on the estimation of selection bias and then addressing the invasive propensity weighting.

Results

Analysis

The analysis is based on the download to browse ratio which comes through the standard deviation for the ranking algorithm. Not only this, it is then followed with the cumulative downloads and the weighted 7-day downloads. There are differences in the DB ratio that corresponds to the difference for more than 10 million downloads. There is cumulative download algorithm with the apps that are for the longer history in the store of app (Milosevic et al., 2017). It comes with the existing users who are then presented with the low D-B ratio. The algorithm is set for the high variable top apps chart with the appearance on the chart which is replaced quickly. The results are from the experiment with the fast-changing top apps that tend to involve the evolutionary developer strategy with experiments that are performed through the developers and the other random features. There are different options that comes through download to browse and then correspond to the standard deviation with the ranking algorithm and the fast-changing apps chart. Here are illustrations about the values which are for the ecosystem matures and then improving the D-B ratios for the cumulative downloads with no improvement for the average D-B ratio. The performance is with the developers producing the apps with the random features and the performances of the algorithm that tend to be remaining unaffected. The focus is on the results with improving the ratios with the producing of the apps with the random features. Here, the trends are set for the minimizing of the effects for the different ranking algorithms. Hence, the algorithms are for all the algorithms except the cumulative downloads, which tend to be equally effective as well. The app store does not present the content effectively for the users and so there are problems about being featured as well (Yerima et al., 2019). The ranking is for the quality of the ranked lists with the major impact on the user satisfaction and the revenue systems as well. The ranking algorithm is for optimizing the performance and then learn about rank methods to solve problems by taking item pair and item lists as the inputs. The interactions are mainly for modelling the mutual influence with the items to refine on the initial lists and then handle the encoding for the intra item patterns for featuring space. The methods are encoded with the Global Rank that comes with limited ability to model the interactions as well. The structure is to make use of the self-attention mechanism where there is interaction with personalized encoding functions that can be considered important for re-ranking systems. The users tend to work on the personalization with Transformer structure to represent the user preferences and the intent for the item interaction. There are concepts related to the users focusing on the price comparison and the different adjustments and the KeyStore functions. The app store search algorithm changes from time to time depending on the user researches. It includes the change or the update of the keywords with testing on the positive reviews as well. The increased performance and fixing the bug issues release to the glitch free update with the new positive reviews from the users with higher visibility and ranking for the app stores. There is a need to focus on the keywords with the app name to improve the visibility and then handle the web-based applications that are offered as a service. Google will then be displaying the bottom of the arrow of the pack where the users can also view the different results. The information is about the good amount of the info about the app in the pack with the display of the app images and the title. The users tend to click and work on optimizing the mobile apps for ranking higher for the search results. The visibility is based on optimizing the mobile applications which are seen to be for the specific or the desired actions (Kim et al., 2018).


Discussion

The expected outcomes and contributions are based on the study of the rationality with the application platforms. The approach is based on the collection of enough data and then handling the analysis with the accurate download information for the apps. The app developers need to focus on the different reasons with extracting the evidences that comes from the app’s historical ranking and the rating with reviewing the records of the ranking of the fraud detection as well. Not only this, there are approaches for the detection of the ranking fraud which comes through leading events. Hence, the approach is based on the global anomaly for the mobile apps with estimating on the credibility as well. The experimental data is for handling the mining leading sessions with the experimental setup as well. For the improvement of the search algorithm, there is an information that is for the web to find on the needs and the Google ranking system is designed for the search index for finding the relevant data as well. It helps in giving the search algorithms with the query, relevance and the page usability. With this, there is a content display and the answering of the queries for the different external Search Quality Raters who are set around the world. The quality of the content and the relevance is based on the context and settings with establishing information and building the better language models. This involves the simple interpretation of the spelling mistakes and then understanding the research for the natural language understanding. The capability is to allow the search with improving on the trending keywords and the freshness algorithms (Kao et al., 2017).

Conclusion

The application is important for the content that is effective to the users and also for the handling of the apps which are listed for too long on the New Apps chart. It comes with the undesirable apps that lingers with the download to browse ratio which will fall. The app ecosystems tend to investigate on the charts with the effectiveness that is depending on the speed where the content is seen to be updated as well. There are measures for the success and also to handle the overwhelming effects for the chart organization. The app has different features where the mobile app ecosystems come from the store perspectives. The approach is based on the mobile app market with fraudulent and deceptive activities on the bumping on the app in the popularity list (Bohnen et al., 2016). The frequent for the app developers with the inflating on the app sales or posting app ratings, with committing ranking fraud. The holistic view of ranking includes the leading session with the detecting on the local anomaly on the ranking based on the review behaviors through the statistical hypothesis tests. The number of mobile apps has been involving the development of mobile apps with the leaderboard that leads to the huge downloads with the revenue. The relying traditional marketing solutions with the resorts with the fraud that boost in the apps with the app with the help of the ranking manipulation. The online review spam detections and the mobile app recommendation includes the problems on the detecting ranking fraud for the mobile apps with the proposed development on the ranking fraud.


Future Work

The ranking records are for handling the apps with the evidences with the limited time discount. The historical rating and the reviewing records with the behaviors that includes ranking fraud detection. The preliminary approach is based on the applied on the ranking threshold with the given mobile app with the defined approach for detecting the fraud leading sessions. The ranking-based evidences includes the historical ranking records with the phases to handle the recession, with the peak position with handling the leading events. The evidences are based on the ranking patterns with the rising phase and the recession phase for the app. The rating-based evidences are based on the apps on the advertising effect. The legal marketing services includes the historical rating records with the user rating which is considered on the features of App advertisement. The review-based evidences are for the allowing users to the textual comments for the app reviews. The reports are related to the capturing to the leading sessions for detecting ranking fraud with the evidence aggregation with the permutation-based models, score-based models with the focus on the global ranking for the candidates. The supervised approach comes through the use of linear combination. Hence, there are evaluation for the final evidence score that involves the range to evaluate the sessions and then detect the fraud rank in the accurate positions.

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