Perturbation based Fuzzified k-Mode Clustering Method for Privacy Preserving Recommender System

Recommender systems are extensively used today to ease out the problem of information overload and facilitate the product selection by users in e-commerce market. Both privacy and security are two major concerns of the user in these systems. For the protection of the user’s rating, there are several existing works on the basis of encryption or randomization methodologies. This paper proposes a methodology that not only protects the privacy of ratings but also provides better accuracy. After applying fuzzification on the user ratings, random rotation and perturbation methods are used before being fed to the collaborative filtering system. In this process, similar users are grouped into clusters by which recommendation is made. By considering different cluster size on four different datasets, the proposed fuzzified k-Mode clustering method provides less MAE and RMSE value as compared to other k-Means and k-Mode clustering approach and also achieves the better privacy than randomized perturbation method by obtaining IVDM value i.e. 0.67, 0.61, 0.55 and 0.7.

ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake.

Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark

With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

Predict App Rank on Google Play Using the Random Forest Method

Application developers and users are the keys to the market impact on application development. In application development, developers need to predict applications in the market accurately, accurate prediction results are very important in showing user ratings that affect the success of an application. Ratings are given by users to judge that the application is good or not. The higher the rating given by the user, it means that the user likes the application and can be a benchmark for other users to download the application. It is undeniable that there are so many apps available on the google play store, it is impossible for users to select one by one app on the google play store. Therefore, a rating prediction system is needed to determine the right application based on the rating given by the user to an application. Predictions will be made using the random forest algorithm as the method used to predict application ratings. This study using the Google Play Store dataset. This dataset has 10840 rows and 13 attributes. The results of this study can be seen from the use of the random forest algorithm with an average accuracy of 93.8%. Keywords: Google Play Store, Rating, Prediction, Random Forest.