Currently, more and more people use music streaming websites to listen to music, and a music recommendation service is commonly provided on the music streaming websites. A good music recommender system improves people’s user experience of music streaming websites. Nevertheless, there are some issues regarding the existing music recommender systems that need to be looked into.The purpose of this thesis is to identify the weaknesses of music recommender systems. Spotify, a Swedish music streaming website, has a large number of users. As it is a widely known streaming service, it seems appropriate for a case study on the drawbacks of music recommender systems.The case study method has been chosen for doing this research. The process of making up this thesis was divided into three stages. At the first stage, some basic preparations for the thesis were done. The second stage was characterized by some empirical work, like interviews and questionnaires, to collect the required data. Those empirical findings were analyzed in the third part to help us to identify and define the drawbacks.The research results presented in this thesis contribute to close several knowledge gaps in the area of music recommender systems and could thus be beneficial to different actors: streaming website operators to identify drawbacks of their recommender system; designers of recommender systems to improve system design; and, last but not least, this thesis provides some useful advice to those who market music streaming websites.This thesis does not focus on the technical and algorithm fields, i.e. the hardware- and software-related background. Instead, the idea and the functions of the recommender system, its feedback loop and the user experience were subject to our research and discussion. The results of the thesis can provide those responsible with both and inspiration for creating more customized recommender systems.

Although there has been much work done in the industry and academia on developing the theory and application of social networks as well as recommender systems, the relation between these research areas is still unclear. An innovative idea, which enables to integrate these areas, and applies recommendation systems to the online social network systems, is proposed in this thesis. Recommendation systems for social networks differ from the typical kinds of recommendation solutions, since they suggest human beings to other ones rather than inanimate goods. Thus, conventional recommendation methods should be enhanced by social features of the networks and their members. This thesis presents the result of the study on the recommendation framework for virtual communities. It also contains an overview of recent approaches to recommendation systems and social networks, as well as description of the online social network systems.

This thesis deals with recommender systems in the field of e-recruitment. The main objective is to design a job recommender system for career portal UNIjobs.cz. First, the theoretical background of recommender systems is provided. In the following part, specific properties of job recommender systems are discussed, as well as existing approaches to recommendation in the e-recruitment environment. The last part of the thesis is dedicated to designing a recommender system for career portal UNIjobs.cz. The output of that part is the main contribution of the thesis.

Computer network complexity has increased in the last decades due to the introduction of various concepts, leaving network maintainers in hardship to manage such huge and tangled networks. In this study, we aim to aid service providers to optimize and automate their networks. Currently, network maintainers perform a vast number of explicit measurements, which has a negative effect on the network’s health and stability. Depending on the service’s nature, measurements are either made at service initiation as in the case of server-client selection or continuously done to monitor the quality of service as in the case of quality assurance applications. We intend to apply artificial intelligence to minimize the dependency on such explicit measurements and hence, optimize the network with minimal cost. From the two types of applications, we focus on distributed delay measurements for Esports server-client selection problem as well as network automation and failure mitigation task done by Internet service providers. In large-scale networks, it is impractical to measure the delay between every node explicitly. As a result, we propose an AI-based delay measurement estimator system. The system’s inputs are just the source and destination nodes’ IP-addresses. Network maintainers continuously monitor their network status to detect any sudden change in the network and take suitable action(s) to keep the network in the best conditions. We propose an ML-based action recommender engine that is able to identify the current network status and suggest a set of actions that restore the network to its optimum state.

Cloud computing has greatly impacted the scientific community and the end users. By leveraging cloud computing, small research institutions and undergraduate colleges are able to alleviate costs and achieve research goals without purchasing and maintaining all the hardware and software. In addition, cloud computing allows researchers to access resources as their teams require and allows real-time collaboration with team members across the globe. Nowadays however, users are easily overwhelmed by the wide range of cloud servers and instances. Due to differences between the cloud server platforms and between instances within the platform, users find it difficult to identify the right instance match for their application. Therefore, we propose the A2Cloud-Hierarchy (A2Cloud-H) framework that recommends Cloud instances to users for high-performance scientific computing. The framework comprises four components: training data collection, supervised learning (SL) module, unsupervised learning (USL) module, and a decision module. The training database comprise testing traces of previous application and Cloud instances; these are contributed by the scientific community. The SL module contains three popular supervised learning modules: logistic regression, support vector machine and random forest, which train using the database to qualitatively assess the instance performance for the target application. The USL module includes three collaborative filtering methods: application-based, instance-based and rank-based, which use the database to estimate the instances’ performance ratings for the target application. The decision module comprises multiple tiers of analytic hierarchy processing, which consolidate the instance recommendation from the SL and USL modules into a final instance recommendation. The model is trained and validated by 8 real-world applications on 20 Cloud instances, yielding more than 90% modeling accuracy. The recommendation and integration method proposed in this thesis can help promote a better cloud computing environment for both end-users and cloud server platforms.

A recommender system is a popular tool used by companies to increase customer satisfaction and to increase revenue. Collaborative filtering and content-based filtering are the two most common approaches when implementing a recommender system, where the former provides recommendations based on user behaviour, and the latter uses the characteristics of the items that are recommended. The aim of the study was to develop and compare different recommender system approaches, for both book and author recommendations and their ability to predict user ratings of an e-book application. The evaluation of the models was done by measuring root mean square error (RMSE) and mean absolute error (MAE). Two pure models were developed, one based on collaborative filtering and one based on content-based filtering. Also, three different hybrid models using a combination of the two pure approaches were developed and compared to the pure models. The study also explored how aggregation of book data to author level could be used to implement an author recommender system. The results showed that the aggregated author data was more difficult to predict. However, it was difficult to draw any conclusions of the performance on author data due to the data aggregation. Although it was clear that it was possible to derive author recommendations based on data from books. The study also showed that the collaborative filtering model performed better than the content-based filtering model according to RMSE but not according to MAE. The lowest RMSE and MAE, however, were achieved by combining the two approaches in a hybrid model.

In this thesis, we show how a text-based Recommendation Systems can greatly benefit from neural statistical language models, more particularly BERT. We evaluate the framework on a digital and collaborative platform for radiologists, by automatically suggesting scientific papers from the medical database PubMed, to provide evidence in diagnostic radiology. The models use contextualized vectors to represent text, accounting for writing style, misspelling and jargon. By using pre-computed representations of text passages, we are able to use compute-heavy statistical language models in production environments, where supercomputers are not available during inference. The results suggest pre-computed embeddings are very effective when the texts came from the same domain, and less effective (but still useful) in capturing the interaction between clinical and scientific text. Nonetheless, the suggested solutions hold promises in this and other areas in medicine. Possibly, the results are transferable to other domains, such as processing of legal documents and patent search.

With the booming development of information technology, text information is not only remained in paper-based forms, but also in digital forms which have been distributed all over internet. Massive information on the internet provides us so many options while at the same time makes it hard for us to choose which detail information we exactly need. The appearance of media monitoring is going to change the situation and help solve the problem. Meltwater group as a media monitoring company provides a service of tracking and sorting information to enterprises and help them to achieve business goals. These goals may include finding the best time or place to do business campaign and knowing the dynamic information about the competitors. There is a recommender system in Meltwater. When a query has been searched, the corresponding documents which are searched from the database will be presented. The problem for the system is that some of the documents have beenturned out to be misclassified and the correctness rate for the recommendation isnot that high. To help solve this problem and make the search better, this paper will introduce a new algorithm which is based on word embedding approach and users’ supervision. The background information of Meltwater group and its existing frame of recommender system will be specifically illustrated at the beginning of the paper. Followed by it will be the exploration of background methods which include LSA (Latent Semantic Analysis), Random Indexing and Word2vec. Besides, the necessary tools such as T-SNE, K-means clustering and hierarchy clustering will also be mentioned in this part. The data sets that are going to be used in this paper will be described after thepart of background methods. Information such as the introduction of the data and the dealing of it will be mentioned in a detail way. The description of the algorithm will appear in the middle of the paper with detail steps. Followed by it is the evaluation. The algorithm will be evaluated by using several different data sets and the confusion matrix will be used as a means of measurement. Finally, a summary of the method as well as future suggestions will be made at the end of the paper.

Growing an online community takes time and effort. Relationships in an online community must be initiated based on trust followed by privacy, and then carefully cultivated. People are using web based social networks more than recent past, but they always want to protect their private data from unknown access; meanwhile also eager to know more people whom they are interested. Among all other system, trust based recommenders have been one of the most used and demanding system which takes the advantage of social trust to generate more accurate predictions. In this work we have proposed for Meven (An Enterprise trust-based profile recommendation with privacy), which uses Social Network Content (User Profiles and trends) with Trust and privacy control policy. The idea of system is to provide Social Networks with the ability to quickly find related information about the users having similar behaviors as the current user. The users will also be able to set the privacy metrics on their profiles so they will not get recommendation of those they feel less important and this is achieved by Privacy metrics. To generate accurate predictions, we defined trust between two users as a strong bond which is computed using different metrics based on user’s activities with respect to different content such as blogging, writing articles, commenting, and liking along with profile information such as organization, region, interests or skills. We have also introduced privacy metric in such a way so that users have full freedom to hide themselves from the recommendation system or they can also have the opportunity to customize their profiles to be visible to certain level of trustworthy users. We have exposed our application as a web service(api) so that any social network web portal can access the recommendations and publish them as a widget in social network.

Recommender System (RS) has become one of the most important component for many companies, such as YouTube and Amazon. A recommender system consists of a series of algorithms which predict and recommend products to users. This report covers the selection of many open source recommender system projects, and movie predictions are made using the selected recommender system. Based on the predictions, a comparison was made between precision and an improved precision algorithm. The selected RS uses singular value decomposition in the field of collaborative filtering. Based on the recommendation results produced by the RS, the comparison between precision and the improved precision algorithms showed that the result of improved precision is slightly higher than precision in different cutoff values and different dimensions of eigenvalues. / Rekommendationssystem har blivit en av de viktigaste beståndsdelar för många företag, såsom YouTube och Amazon. Ett rekommendationssystem består av en serie av algoritmer som förutsäger och rekommenderar produkter till användare. Denna rapport omfattar valet av många öppen källkod rekommendationssystem projekt, och filmprognoser görs med det valda rekommendationssystemet. Baserat på filmprognoser, gjordes en jämförelse mellan precision och en förbättrad precision algoritmer. Det valda rekommendationssystemet använder singulärvärdesuppdelning som kollaborativ filtrering. Baserat på rekommendationsresultat som produceras av rekommendationssystemet, jämförelsen mellan precision och den förbättrade precisions algoritmer visade att resultatet av förbättrad precision är något högre än precision i olika brytvärden och olika dimensioner av egenvärden.