L’objectif de cette thèse empirique est de contribuer à une meilleure compréhension du marché de la téléphonie mobile en étudiant les stratégies d’offres (et notamment la tarification) des opérateurs, ainsi que les comportements des consommateurs en termes d’usages et de recommandation. / We wish to contribute to the understanding of the mobile market by studying the pricing strategies of the operators, but also by studying the consumers behavior in terms of uses and recommendation.

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.

In recent decades, economists are gradually showing their interests in the study of happiness. They even put forward some challenges to the traditional theories. In contrast, studies on Chinese happiness problem are not enough in terms of breadth and depth.  This paper used the data provided by China General Social Survey to conduct an empirical analysis. The model author adopted is Ordered Discrete Choice model. In the empirical section, author analyzed the impact of income, macroeconomic variables, etc.  Ultimately, based on the empirical results, author proposed some policy recommendations and further study suggestions.

Recommendation systems typically use historical interactions between users and items topredict what other items can be of interest to a user. The recommendations are based onpatterns in how users interact similarly with items. This thesis investigates if it is possible toimprove the quality of the recommendations by including more information about the items inthe model that predicts the recommendations. More specifically, the use of deep learning toextract information from item images is investigated. To do this, two types of collaborativefiltering models, based on historic interactions, are implemented. These models are thencompared to different collaborative filtering models that either make use of user and itemattributes, or images of the items. Three pre-trained image classification models are used toextract useful item features from the item images. The models are trained and evaluated using adataset of historic transactions and item images from the online sports shop Stadium, given bythe thesis supervisor. The results show no noticeable improvement in performance for themodels using the images compared to the models without images. The model using the userand item attributes performs the best, indicating that the collaborative filtering models can beimproved by giving it more information than just the historic interactions. Possible ways tofurther investigate using the image feature vectors in collaborative filtering models, as well asusing them to create better item attributes, are discussed and suggested for future work.

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.

E-commerce is an ever growing industry as the internet infrastructure continues to evolve. The benefits from a recommendation system to any online retail store are several. It can help customers to find what they need as well as increase sales by enabling accurate targeted promotions. Among many techniques that can form recommendation systems, this thesis compares Collaborative Filtering against Association Rule Mining, both implemented in combination with clustering. The suggested implementations are designed with the cold start problem in mind and are evaluated with a data set from an online retail store which sells clothing. The results indicate that Collaborative Filtering is the preferable technique while associated rules may still offer business value to stakeholders. However, the strength of the results is undermined by the fact that only a single data set was used. / E-handel är en växande marknad i takt med att Internet utvecklas samtidigt som antalet användare ständigt ökar. Antalet fördelar från rekommendationssytem som e-butiker kan dra nytta av är flera. Samtidigt som det kan hjälpa kunder att hitta vad de letar efter kan det utgöra underlag för riktade kampanjer, något som kan öka försäljning. Det finns många olika tekniker som rekommendationssystem kan vara byggda utifrån. Detta examensarbete ställer fokus på de två teknikerna Collborative Filtering samt Association Rule Mining och jämför dessa sinsemellan. Båda metoderna kombinerades med klustring och utformades för att råda bot på kallstartsproblemet. De två föreslagna implementationerna testades sedan mot en riktig datamängd från en e-butik med kläder i sitt sortiment. Resultaten tyder på att Collborative Filtering är den överlägsna tekniken samtidigt som det fortfarande finns ett värde i associeringsregler. Att dra generella slutsatser försvåras dock av att enbart en datamängd användes.

Teknik och data är nyckeln till att Bonnier Business Media (BBM) ska kunna nå sina mål och leverera ytterligare tillväxt. Därför vill man ligga i framkant när det gäller att undersöka nya tekniker som kan förbättra plattformarna och göra dem mer tidsenliga. BBM har bland annat velat ta fram ett rekommendationssystem som ska användas till att göra innehållet individanpassat på webbplatserna och på ett effektivt sätt presentera detta så att de olika målgrupperna får den information de förväntar sig. Till exempel ska besökaren kunna få förslag på artiklar och evenemang som kan vara av intresse. Målet med detta examensarbete har varit att ta fram en prototyp för ett rekommendationssy- stem med tillhörande algoritmer. Prototypen skulle kunna användas som ett “koncepttest” för att undersöka möjligheten att skapa personliga rekommendationer till läsare på Veckans Affärers webbplats, va.se. Implementationen av rekommendationssystem som togs fram till BBM bestod av en objektbaserad kollaborativ filtrerings algoritm som använde besökarnas beteende, publiceringsdatum och popularitet på artiklarna och evenemangen för att skapa individuella rekommendationer. Efter genomförda tester och analyser visar resultatet att det är fullt möjligt att skapa personliga rekommendationer som har en högre precision än vad ett grundläggande rekommendationssystem, till exempel en popularitetslista, kan erbjuda. / Technology is the key for Bonnier Business Media (BBM) to reach their goals and deliver future growth. Therefore they want to be in the very forefront when it comes to exploring new technologies that can improve their platforms and make them more up to date. BBM has among other things aimed to develop a recommendation system that is supposed to make the content of their web sites personalized and in an efficient way present this so that the different target groups will get the information that they expect. For example the visitor should be able to get suggestions on articles and events that might be of interest. The aim of this thesis has been to develop a prototype of a recommendation system with associated algorithms. The prototype could be used as to examine the possibility to create personalized recommendations for the readers on BBM:s website va.se (Veckans Affärer). The implementation of the recommendation system that was developed for BBM consisted of an object-based collaborative filtering algorithm using visitor behavior, publication date and popularity of articles and events to create personalized recommendations. After com- pleting tests and analyzes the results show that it is possible to create recommendations with a higher precision than a basic recommendation system, like a popularity list, can of- fer.

This master’s thesis concerns how to recommend multimedia content which a user might view – with some media player. It describes how a computer application (called a recommendation engine) can generate better recommendations for users based on using information available from social networks and the media selections made by others. This thesis gives an introduction to the area of “recommendations”, recommendation engines, and social networks. An overview of existing recommendation techniques suggests potential solutions to the problem of what recommendations to make to a given user. The thesis presents how social networks can be used to further enhance the users’ experience and describes the work that has been done to realize this recommendation system. An evaluation of the implemented solution is given. The thesis concludes with a summary of how recommendation engines and social network technologies can be used and suggests some future work. This thesis is of current interest since there is a tremendous quantity of content which is being offered in stores and via services on the web. A recommendation system makes it easier for users to find content which they find appropriate. Since social network communities are growing rapidly there has been an interest to use this information to get recommendations from friends. The results from the evaluation of the prototype recommendation system show how social networks which utilize trust systems might affect the recommendation which is given. / Det här examensarbetet behandlar hur man kan rekommendera multimedia som en användare kan tänkas titta på. Den beskriver hur en dataapplikation (kallad rekommendationsmotor) kan generera bättre rekommendationer åt en användare genom att använda information från sociala nätverk, och andra användares tidigare val av media. Examensarbetet ska ge en introduktion in om ämnet ”rekommendationer”, rekommendationsmotorer och sociala nätverk. En överblick av existerande rekommendationstekniker ger potentiella lösningar till problem för rekommendationer till en användare. Examensarbetet ska även presentera hur sociala nätverk vidare kan förbättra användarupplevelsen och beskriva arbeten som har gjorts för att realisera. En evaluering av det implementerade systemet kommer att ges. En slutsats om hur rekommendations motorer och sociala nätverk kan användas och hur man kan fortsätta på arbetet kommer avsluta rapporten. Examensarbetet är intressant eftersom det finns rikligt av tjänster och produkter på nätet som utnyttjar rekommendationsmotorer. Rekommendationssystem ser till att användare enklare kan hitta information som anses lämplig för användaren. Eftersom även sociala nätverk är en växande trend finns det intresse att använda den här informationen till att ge rekommendationer från vänner. Resultatet från evalueringen av det utvecklade systemet kommer att visa en intressant slutsats om hur sociala nätverk som utnyttjar ”trust” system kan påverka rekommendationen.

Recommendation systems become more and more necessary due to the growing supply chain. Therefore, scientists are developing models that can serve different recommendation needs faster than before, and it is getting more complicated to choose the model for a specific case. In this thesis, there are three neural collaborative filtering methods compared regarding dataset fit. This research shows that there is no one-fits-all method. There is much space for improvement in all the areas: dataset selection and aggregation, method development and operation, and selective approaches for the analysis of the results. In the thesis, three contrasting datasets are chosen (Chess, Library, and LastFM), and three novel approaches are tested: recently released Dynamic Graph Collaborative Filtering (DGCF) and Dynamic Embeddings for Interaction Prediction (DeePRed) are compared to the Joint Dynamic User- Item Embeddings (JODIE) as the baseline. Results show DeePRed being a state-of-the-art model that outperforms other methods. It runs an epoch for a small dataset in less than a minute, shows great prediction accuracy in an average of 98% for small datasets. However, DGCF does not show accuracy improvement over JODIE but is significantly faster for an extensive dataset. / Rekommendationssystem blir mer och mer nödvändiga på grund av den växande försörjningskedjan. Därför utvecklar forskare modeller som kan tjäna olika rekommendationsbehov snabbare än tidigare och det blir mer och mer komplicerat att välja modell för ett specifikt fall. I denna avhandling finns det tre neurologiska samarbetsfiltreringsmetoder som jämförs avseende deras gran för olika datamängder. Denna forskning visar att det inte finns någon metod som passar alla och det finns mycket utrymme för förbättring inom alla områden: datasatsval och aggregering, metodutveckling och drift och selektiva metoder för analys av resultaten. I avhandlingen väljs tre kontrasterande datamängder (Chess, Library och LastFM) och tre nya metoder testas: nyligen släppt Dynamic Graph Collaborativefiltering (DGCF) och Dynamic Embedding for Interaction Prediction (DeePRed) jämförs med Joint Dynamic User-Item. Inbäddning (JODIE) som baslinje. Resultaten visar att (DeePRed) är en avancerad modell som överträffar andra metoder som snabba genom att köra en epok för liten dataset på mindre än en minut, vilket visar stor förutsägelsesnoggrannhet i genomsnitt 98% för små datamängder. Men (DGCF) visar inte förbättring av noggrannhet jämfört med (JODIE), men är betydligt snabbare för en stor dataset.

For any unemployed person in Sweden that is looking for a job, the most common place they can turn to is the Swedish Public Employment Service, also known as Arbetsförmedlingen, where they can register to get help with the job search process. Occasionally, in order to land an employment, the person might require extra guidance and education, Arbetsförmedlingen outsource this education to external companies called providers where each person gets assigned a coach that can assist them in achieving an employment quicker. Given the current labour market data, can the data be used to help optimize and speed up the job search process? To try and help optimize the process, the labour market data was inserted into a graph database, using the database, a recommendation system was built which uses different methods to perform each recommendation. The recommendations can be used by a provider to assist them in assigning coaches to newly registered participants as well as recommending activities. The performance of each recommendation method was evaluated using a statistic measure. While the user-created methods had acceptable performance, the overall best performing recommendation method was collaborative filtering. However, there are definitely some potential for the user-created method, and given some additional testing and tuning, the methods can surely outperform the collaborative filtering method. In addition, expanding the database by adding more data would positively affect the recommendations as well.