Recommender systems are used on many different websites today and are mechanisms that are supposed to accurately give personalized recommendations of items to a set of different users. An item can for example be movies on Netflix. The purpose of this paper is to implement an algorithm that fulfills five stated goals of the implementation. The goals are as followed: the algorithm should be easy to implement, be effective on query time, accurate on recommendations, put little expectations on users and alternations of algorithm should not have to be changed comprehensively. Slope One is a simplified version of linear regression and can be used to recommend items. By using the Netflix Prize data set from 2009 and the Root-Mean-Square-Error (RMSE) as an evaluator, Slope One generates an accuracy of 1.007 units. The Weighted Slope One, which takes the relevancy of items into the calculation, generates an accuracy of 0.990 units.  Adding Weighted Slope One to the Slope One implementation can be done without changing the fundamentals of the Slope One algorithm. It is nearly instantaneous to generate a recommendation of a movie with regular Slope One and Weighted Slope One. However, a precomputing stage is needed for the mechanism. In order to receive a recommendation of the implementation in this paper, the user must at least have rated two items. / Rekommendationssystem används idag på många olika hemsidor, och är en mekanism som har syftet att, med noggrannhet, ge en personlig rekommendation av objekt till en mängd olika användare. Ett objekt kan exempelvis vara en film från Netflix. Syftet med denna rapport är att implementera en algoritm som uppfyller fem olika implementationsmål. Målen är enligt följande: algoritmen ska vara enkel att implementera, ha en effektiv tid på dataförfrågan, ge noggranna rekommendationer, sätta låga förväntningar hos användaren samt ska algoritmen inte behöva omfattande förändring vid alternering.  Slope One är en förenklad version av linjär regression, och kan även användas till att rekommendera objekt. Genom att använda datamängden från Netflix Prize från 2009 och måttet Root-Mean-Square-Error (RMSE) som en utvärderare, kan Slope One generera en precision på 1.007 enheter. Den viktade Slope One, som tar hänsyn till varje föremåls relevans, genererar en precision på 0.990 enheter. När dessa två algoritmer kombineras, behövs inte större fundamentala ändringar i implementationen av Slope One. En rekommendation av något objekt kan genereras omedelbart med någon av de två algoritmerna, dock krävs det en förberäkningsfas i mekanismen. För att få en rekommendation av implementationen i denna rapport, måste användaren åtminstone ha värderat två objekt.

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.

Recommendation systems can today help users to find what they are looking for in an online store or online service. The companies want to create engagement with the recommendations in the form of interactions. The aim of this thesis is to investigate how to create engagement from the users. For this, the concept of a system will be made. The concept will build on knowledge and data gathered from interviews of owners of an online store or online service and a survey, where the respondents are customers. The results show that different types of information and recommendations fit in different types of situations. That the content of emails needs to stand out in order to create engagement. If the product or service is something that the customers often search for themselves then it is a good option to advertise this through the companies own channels such as web page or app. If the product is something that is more general an ad on social media can be one way to advertise it in order to create engagement from the customers.

Recommender systems are without a doubt a staple of the modern internet. Services like Amazon, Netflix, YouTube and Spotify rely on them. What makes them so engaging that millions of users spent billions of hours on them every day? User engagement is widely accept as a core concept of user experience but we still don’t know what role the user interface plays into it. This thesis investigates the effect of explanations in recommender systems on the users engagement with a case study on BMW Financial Services Thailand’s recommender system. An experiment on Amazon Mechanical Turks with the User Engagement Scale and A/B testing with Google Analytics proved a significant influence of explanations on the users engagement.

Indiana University-Purdue University Indianapolis (IUPUI) / Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences) Vaal University of Technology. / Mobile phone recommendation systems are of great importance for mobile operators to achieve a profit. In a user-derived market, the number of contract users and contract phones is especially significant for mobile service operators. The tremendous growth in the number of available mobile cellular telephone contracts necessitates the need for a recommender system to help users discover suitable contracts on the basis of their usage patterns. Recommender systems recommend items to users and their primary purpose is to increase sales and recommend items that are predicted to be suitable for individual users. There are two commonly used techniques in developing recommender systems including collaborative- and content-based filtering. Recommender systems make their recommendations based on data that is available on the system. These systems have gained popularity over the years and they have been adopted in many domains. In this study a recommender system for mobile subscriber provisioning was developed using a hybrid J48 and kmeans algorithms. The J48 algorithm was used for classifying subscribers per usage stream and then k-means was used to cluster all the subscribers of similar usage patterns. The algorithms were selected after being compared with other algorithms and the two performed best in their categories. The clustering algorithm, k-means, was able to cluster the sample data as follows: Cluster 0 contained 48% (1621) of the subscribers cluster 1 contained 42% (1423) subscribers, cluster 2 contained 8% (272) subscribers and lastly cluster 3 contained 74 subscribers representing 2% of the population and the run time of k-means is faster than that of EM. The classification algorithm j48 performed at an average of 99.98% for correctly classifying instances and this was higher than the Naïve Bayes, zeroR and MLP algorithms. The developed recommender system was able to successfully recommend contract packages to subscribers. A precision-recall curve was produced, and it showed good performance of the system. This study successfully highlighted the challenges in recommender systems, and showed that a hybrid system was better able to recommend products to the mobile subscribers.

No description available.

Recommender systems have become an integral part of the user interface of many web applications. Recommending items to buy, media to view or similar “next choice”-recommendations has proven to be a powerful tool to improve costumer experience and engagement. One common technique to produce recommendations called Collaborative Filtering makes use of the unsupervised Nearest Neighbor-algorithm, where a costumers historic use of a service is encoded as a vector and recommendations are made such that if followed the resulting behaviour-vector would lie closer to the nearest neighboring vectors encoding other costumers. This thesis describes the adaptation of a Collaborative Filtering recommender system to a cyber security vulnerability report setting with the goal of producing recommendations regarding which of a set of found vulnerabilities to prioritize for mitigation. Such an error report scenario presents idiosyncrasies that do not allow a direct application of common recommender system algorithms. This work was carried out in collaboration with the company Detectify, whose product allows users to check for vulnerabilities in their internet facing software, typically web pages and apps. The finding mitigation priorities of historic customers have to be inferred from differences in their consecutive reports, i.e. from noisy vector valued signals. Further, as opposed to the typical e-commerce or media streaming scenario, as a user can not freely choose which item to increase their consumption of, instead, a user can only attempt to decrease their inventory of a limited subset (the vulnerabilities in their report) of all items (all possible vulnerabilities). This thesis presents an adapted Collaborative Filtering algorithm applicable to this scenario. The chosen approach to the algorithm is motivated by an extensive literature review of the current state of the art of recommender systems. To measure the performance of the algorithm, test data is produced which allows for comparison between recommendations based on noisy data and the actual change in a noiseless version. The results that are showcased give reference values as to under what levels of noise and data sparsity the developed algorithm can be expected to produce recommendations that align well with historic behavioural patterns of other customers. This thesis thus provides a novel variation of the Collaborative Filtering algorithm that extends its usability to a scenario that has not been previously addressed in the reviewed literature. / Rekommendationssystem är idag en självklar del av manga användargränssnitt. Exempel på dessa som många av oss interagerar med dagligen är system som föreslår nästa ord när vi skriver, nästa produkt när vi handlar online eller nästa media när vi använder streaming-tjänster. En vanlig teknik för att producera rekommendationer är Collaborativer Filtering, vilken använder Nearest Neighbor-algoritmer för att rekommendera så att en användares historik (beskriven som en vektor) förflyttas närmre de närmaste grannarna om rekommendationen följs. I denna uppsats redovisas en anpassning av ett Collaborative Filtering-rekommendationssystem för användning i samband med skanning efter it-säkerhetsrisker, med målet att producera rekommendationer rörande vilken säkerhetsrisk som bör prioriteras för åtgärd. Ett sådant error report scenario (riskrapport-scenario) för med sig vissa skillnader jämfört med ett e-handel/streaming-scenario som gör det nödvändigt att anpassa de typiska Collaboritve Filtering-systemet innan det är applicerbart. Det här arbetet utförs i samarbete med företaget Detectify, som tillhandahåller en produkt med vilken användare kan upptäcka säkerhetsrisker i deras internet-kopplade mjukvara (exempelvis hemsidor och web-applikationer). Historiska prioriteringar rörande åtgärdande av säkerhetsrisker måste beräknas ut tidigare användares rapporter om hunna risker, alltså från brusiga vektor-värda signaler. En användare kan inte heller fritt välja att öka sin konsumption av någon produkt i ett sortiment, utan istället måste en rekommendation röra vilket objekt i en användares befintliga innehav (de funna riskerna i deras senaste rapport) som användaren bör försöka minska antalet av. I den här uppsatsen presenteras ett Collaborative Filtering-rekommendationssystem anpassat till detta scenario. Algoritmen motiveras med en extensiv litteraturstudie av relevant litteratur och utvärderas med syntetisk data vilket möjliggör undersökning av hur olika nivåer av brus och gleshet (sparsity) inverkar på rekommendationerna. Resultaten som presenteras tillhandahåller referensnivåer för under vilken grad av brus och gleshet algoritmen kan förväntas prestera väl. Sammanfattningsvis utvecklas, utvärderas och presenteras en modifikation av Cillaborative Filtering-rekommendationssystem som möjliggör dessa användade i ett scenario som ej beskrivs i den genomgångna litteraturen.

A common belief is that the larger the variety of options, the better. However, humans have a limited cognitive capacity, which can lead to people experiencing choice overload (Iyengar and Lepper, 2000). Limiting the number of options to reduce choice overload is not a feasible solution. While there have been studies in regards to minimising choice overload with a help of recommender systems, aspects of how the choice set is presented in the user interface received little attention. This study aimed to investigate three commonly used user interfaces (UI); a single-list, a grid and a multi-list. An online experiment was conducted to answer the research questions. Results from this study imply that the single-list UI performed the worst across chosen measures, and was most likely to cause choice overload among participants. Multi-list and grid UI reported better performance and were less likely to cause choice overload among participants.

No description available.