A Recommendation System as a Digital Marketing tool for Online Communities

Recommender systems are able to predict users’ preferences and items of interest, by analysing historical data on their behaviour and actions. Different techniques exist and are applicable in different scenarios. This thesis explores how to combine Content-Based and Collaborative-Filtering techniques in a hybrid system and how personalised recommendations and one-to-one marketing techniques can lead to an improvement in user engagement. Specifically, it is analysed the case of online platforms where there is no rating system in place. Results are empirically tested and evaluated with training/testing approach and recommendations seem to be quite accurate. However, further online evaluation is needed to measure any actual increase in user engagement

Predicting Customer Retention of an App-Based Business Using Supervised Machine Learning

Identification of retainable customers is very essential for the functioning and growth of any business. An effective identification of retainable customers can help the business to identify the reasons of retention and plan their marketing strategies accordingly. This research is aimed at developing a machine learning model that can precisely predict the retainable customers from the total customer data of an e-learning business. Building predictive models that can efficiently classify imbalanced data is a major challenge in data mining and machine learning. Most of the machine learning algorithms deliver a suboptimal performance when introduced to an imbalanced dataset. A variety of algorithm level (cost sensitive learning, one class learning, ensemble methods ) and data level methods (sampling, feature selection) are widely used to address the class imbalance in the retention prediction problems. This research employs a quantitative and inductive approach to build a supervised machine learning model that addresses the class imbalance problem and efficiently predict the customer retention. The retention Precision is used as the evaluation metrics for this research. The research evaluates the performance of different sampling methods (Random Under – Sampling, Random Over – Sampling, SMOTE) on different single and ensemble machine learning models. The results show that Random Under-Sampling used along with XGBoost classifier yields the best precision in identifying the retention class. The best model evolved in the research was also used to predict retainable customers from the recent unknown customer data, and could attain a retention precision of 57.5%

Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.

Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.

Solving cardinality constrained portfolio optimisation problem using genetic algorithms and ant colony optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn this thesis we consider solution approaches for the index tacking problem, in which we aim to reproduces the performance of a market index without purchasing all of the stocks that constitute the index. We solve the problem using three different solution approaches: Mixed Integer Programming (MIP), Genetic Algorithms (GAs), and Ant-colony Optimization (ACO) Algorithm by limiting the number of stocks that can be held. Each index is also assigned with different cardinalities to examine the change to the solution values. All of the solution approaches are tested by considering eight market indices. The smallest data set only consists of 31 stocks whereas the largest data set includes over 2000 stocks. The computational results from the MIP are used as the benchmark to measure the performance of the other solution approaches. The Computational results are presented for different solution approaches and conclusions are given. Finally, we implement post analysis and investigate the best tracking portfolios achieved from the three solution approaches. We summarise the findings of the investigation, and in turn, we further improve some of the algorithms. As the formulations of these problems are mixed-integer linear programs, we use the solver ‘Cplex’ to solve the problems. All of the programming is coded in AMPL

Web and knowledge-based decision support system for measurement uncertainty evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn metrology, measurement uncertainty is understood as a range in which the true value of the measurement is likely to fall in. The recent years have seen a rapid development in evaluation of measurement uncertainty. ISO Guide to the Expression of Uncertainty in Measurement (GUM 1995) is the primary guiding document for measurement uncertainty. More recently, the Supplement 1 to the "Guide to the expression of uncertainty in measurement" – Propagation of distributions using a Monte Carlo method (GUM SP1) was published in November 2008. A number of software tools for measurement uncertainty have been developed and made available based on these two documents. The current software tools are mainly desktop applications utilising numeric computation with limited mathematical model handling capacity. A novel and generic web-based application, web-based Knowledge-Based Decision Support System (KB-DSS), has been proposed and developed in this research for measurement uncertainty evaluation. A Model-View-Controller architecture pattern is used for the proposed system. Under this general architecture, a web-based KB-DSS is developed based on an integration of the Expert System and Decision Support System approach. In the proposed uncertainty evaluation system, three knowledge bases as sub-systems are developed to implement the evaluation for measurement uncertainty. The first sub-system, the Measurement Modelling Knowledge Base (MMKB), assists the user in establishing the appropriate mathematical model for the measurand, a critical process for uncertainty evaluation. The second sub-system, GUM Framework Knowledge Base, carries out the uncertainty evaluation process based on the GUM Uncertainty Framework using symbolic computation, whilst the third sub-system, GUM SP1 MCM Framework Knowledge Base, conducts the uncertainty calculation according to the GUM SP1 Framework numerically based on Monte Carlo Method. The design and implementation of the proposed system and sub-systems are discussed in the thesis, supported by elaboration of the implementation steps and examples. Discussions and justifications on the technologies and approaches used for the sub-systems and their components are also presented. These include Drools, Oracle database, Java, JSP, Java Transfer Object, AJAX and Matlab. The proposed web-based KB-DSS has been evaluated through case studies and the performance of the system has been validated by the example results. As an established methodology and practical tool, the research will make valuable contributions to the field of measurement uncertainty evaluation.Brunel Universit