M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Too many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people‟s preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System.

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Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.

Despite the many technological advancements made in the music industry in recent years, there is still not a single widely adopted platform for musicians to play music together online. In 2020, the Covid-19 pandemic and the subsequent quarantine pushed the need for such a platform into the spotlight. As a response, the music company Elk Audio launched their new product: Aloha. Aloha is a combined hardware and web application that allows musicians to play music in real-time over the Internet. Aloha is currently only intended for musicians who already know each other to connect and play. However, Elk's ambition is to make it the go-to platform for musicians to expand their network.  The purpose of this Master's Thesis is to design the next version of the web application of Aloha, focusing on social interactions. This Master's Thesis investigates musicians' current social and musical behaviour, and their opinions of how this can be done online. Qualitative data was collected by performing semi-structured interviews with musicians of different backgrounds. The study revealed many different goals and needs of potential users of Aloha. Furthermore, we found several determining factors which enable and encourage musicians to form new musical relationships online. The final suggested design is based on the analysed data and founded in theory regarding persuasive and recommending system design, among others. / Trots de tekniska framstegen som gjorts inom musikindustrin de senaste åren, finns det fortfarande inte ett enda allmänt accepterat alternativ för musiker att spela musik tillsammans online. Covid-19 pandemin och den åtföljande karantänten förde behovet för en sådan plattform till rampljuset. I början av år 2020 insåg musikföretaget Elk Audio att de kunde fylla denna lucka med sin nya produkt: Aloha. Aloha är en kombinerad hårdvara och webbapplikation som möjliggör musiker att spela musik i realtid över Internet. Aloha är för närvarande endast avsedd för musiker som redan känner varandra. Elks ambition är dock att göra Aloha till en plattform för musiker där de kan utöka sitt musikaliska nätverk. Syftet med detta examensarbete är att utforma nästa version av Alohas webbapplikation, med fokus på sociala interaktioner. Detta examensarbete undersöker musikers nuvarande sociala och musikaliska beteenden och deras åsikter om musikaliska interaktioner online. Kvalitativa data samlades in genom att utföra halvstrukturerade intervjuer med musiker från olika bakgrunder. Studien avslöjade de många olika målen och behoven hos potentiella användare av Aloha. Dessutom fann vi flera avgörande faktorer som möjliggör och uppmuntrar musiker att skapa och underhålla nya musikrelationer online. Den slutliga föreslagna designen baseras på det analyserade datat och grundas i teori om design av rekommendationssystem, m. fl..