3. Electronic Theses and Dissertations (ETDs) - All submissions
Permanent URI for this communityhttps://wiredspace.wits.ac.za/handle/10539/45
Browse
1 results
Search Results
Item Providing value-added services to cellphone contract clients - a hybrid recommendation approach(2016) Ndlovu, MpumeleloThere is stiff competition for customers and market share in the South African telecommunications industry amongst the four predominant mobile service providers, namely Vodacom, MTN, Cell C and Telkom Mobile. The First National Bank (FNB) through one of its entities, FNB Connect, has also joined this intensely competitive environment. These companies face a constant challenge of having to come up with new and innovative ways of attracting new customers and retaining their current ones. Cell C has embarked on a good strategy of claiming solid market share and growing itself against the competition by using the Private Label Promotions (PLP) group, a leading BEE Level 3 company that provides a variety of business solutions, to market GetMore, its value-added service package. A recommender system could be used to suggest and promote the items available in this package to existing and potential clients (users). There are different approaches to recommendation, the most widely used ones being the collaborative and content-based recommendation. The collaborative filtering approach uses the ratings of other users to recommend the items the current (active) user might like. In the content-based approach, items are recommended in terms of their content similarity to items a user has previously liked, or elements that have matched a user’s attributes (features). Hybrid recommendation approaches are used To eliminate the drawbacks individually associated with the CF and CBF approaches and to leverage their advantages. One of the aims of this research was to design and implement a prototype hybrid recommender system that would be used to recommend Cell C’s GetMore package to current and potential subscribers. The system was to implement matrix factorisation (collaborative) and cosine similarity (content-based) techniques. Several experiments were conducted to evaluate its performance and quality. The metrics used included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Area Under the ROC Curve (AUC). We expected the proposed hybrid recommender system would leverage the advantages provided by its different components and demonstrate its effectiveness in providing Cell C’s customers with accurate and meaningful recommendations of its GetMore package services. Keywords: Content-based Recommendation, Collaborative Recommendation, Hybrid Recommendation, Cosine Similarity, Matrix Factorisation, Association Rule Mining, J48 Classifier, Decision Table, Naive Bayes, Simple K-means, Expectation Maximization, Farthest First, Predictive Apriori