Masters Degrees (Statistics and Actuarial Science)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/733

Browse

Browsing Masters Degrees (Statistics and Actuarial Science) by Author "Beukman, Erika"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Improving collaborative filtering with fuzzy clustering
    (Stellenbosch : Stellenbosch University, 2021-12) Beukman, Erika; Steel, Sarel Johannes; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY : Recommender systems are machine learning algorithms widely used across various industries to predict user preference for sets of items in order to recommend items to the user. Since it narrows down the entire space of items to a list of items that the client might prefer, it can be seen as an information filtering system. The main purpose of this is twofold: firstly, to introduce new items to users that they might not have otherwise come across, thereby increasing user engagement with products and services, and secondly, to improve user experience. The focus in this report is on collaborative filtering, one of the main approaches to the recommender system problem. A broad range of collaborative filtering techniques is available, including the use of factorization machines. This technique is studied in the research. Factorization machines offer several advantages, one of which is the ease with which information outside of the traditional ratings matrix can be included into the filtering system. Output generated from a fuzzy clustering of users is investigated within the context of a movie recommendation scenario. The positive role which these variables can play in a recommender system is clearly illustrated.