A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features

AbstractCollaborative filtering (CF) either memory based or model based, has been emerged as an information filtering tool that provides effective recommendations to users utilizing the experiences and opinions of their similar neighbors when they interact with large information spaces. Memory based CF is more accurate than model based CF but it is less scalable. Our work in this paper is an attempt towards introducing a recommendation strategy (FPSO-CF) based on user hybrid features that retains the accuracy of memory – based CF as well as the scalability of model-based CF in an efficient manner. Since most user features are imprecise in nature, therefore these can be represented more naturally by using fuzzy sets. In this work, we employ particle swarm optimization algorithm (PSO) to learn user weights on various features and use fuzzy sets for representing user features efficiently. Effectiveness of our proposed RS (FPSO-CF) is demonstrated through experimental results in terms of various performance measures using the MovieLens dataset

Clustering in Recommendation Systems Using Swarm Intelligence

Ένα σύστημα συστάσεων είναι μία εφαρμογή που εκμεταλλεύεται πληροφορίες για να βοηθήσει τους χρήστες στη λήψη αποφάσεων προτείνοντας αντικείμενα που μπορεί να τους αρέσουν. Ένα σύστημα συστάσεων που βασίζεται στην τεχνική του συνεργατικού φιλτραρίσματος (collaborative filtering) δημιουργεί συστάσεις στους χρήστες με βάση τις προτιμήσεις παρόμοιων χρηστών. Ωστόσο, αυτός ο τύπος συστήματος συστάσεων δεν είναι τόσο αποτελεσματικός όταν τα δεδομένα αυξάνονται σε μεγάλο βαθμό (scalability) ή όταν δεν υπάρχει αρκετή πληροφορία (sparsity), καθώς δεν ομαδοποιούνται σωστά οι παρόμοιοι χρήστες. Αυτή η διπλωματική εργασία προτείνει τρείς υβριδικούς αλγορίθμους που ο καθένας συνδυάζει τον αλγόριθμο k-means με έναν αλγόριθμο ευφυΐας σμήνους για να βελτιώσει την ομαδοποίηση των χρηστών, και κατ’ επέκταση την ποιότητα των συστάσεων. Οι αλγόριθμοι ευφυΐας σμήνους που χρησιμοποιούνται είναι o αλγόριθμος τεχνητής κοινωνίας μελισσών (artificial bee colony), ο αλγόριθμος βελτιστοποίησης αναζήτησης κούκων (cuckoo search optimization) και ο αλγόριθμος βελτιστοποίησης γκρίζων λύκων (grey-wolf optimization). Οι προτεινόμενες μέθοδοι αξιολογήθηκαν χρησιμοποιώντας ένα σύνολο δεδομένων του MovieLens. Η αξιολόγηση δείχνει πως τα προτεινόμενα συστήματα συστάσεων αποδίδουν καλύτερα σε σύγκριση με τις ήδη υπάρχουσες τεχνικές όσον αφορά τις μετρικές του μέσου απόλυτου σφάλματος (mean absolute error - MAE), της ακρίβειας (precision), του αθροίσματος των τετραγωνικών σφαλμάτων (sum of squared errors - SSE) και της ανάκλησης (recall). Επιπλέον, τα αποτελέσματα της αξιολόγησης δείχνουν πως ο υβριδικός αλγόριθμος που χρησιμοποιεί την μέθοδο της τεχνητής κοινωνίας μελισσών αποδίδει ελαφρώς καλύτερα από τους άλλους δύο προτεινόμενους αλγορίθμους.A recommender system (RS) is an application that exploits information to help users in decision making by suggesting items they might like. A collaborative recommender system generates recommendations to users based on their similar neighbor’s preferences. However, this type of recommender system faces the data sparsity and scalability problems making the neighborhood selection a challenging task. This thesis proposes three hybrid collaborative recommender systems that each one combines the k-means algorithm with a different bio-inspired technique to enhance the clustering task, and therefore to improve the recommendation quality. The used bio-inspired techniques are artificial bee colony (ABC), cuckoo search optimization (CSO), and grey-wolf optimizer (GWO). The proposed approaches were evaluated over a MovieLens dataset. The evaluation shows that the proposed recommender systems perform better compared to already existing techniques in terms of mean absolute error (MAE), precision, sum of squared errors (SSE), and recall. Moreover, the experimental results indicate that the hybrid recommender system that uses the ABC method performs slightly better than the other two proposed hybrid algorithms

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio

Modeling document classification to automate mental health diagnosis

The objective of this study is to determine if diagnosis documents can be used with document classification to automatically diagnose mental health conditions. Document classification allows text documents to be analyzed and organized into their appropriate classes based on the features and words presented in the text. One application of this is within the medical field to automatically classify different patient diagnosis based on medical or patient notes. This research applied mental health diagnosis documents to automatically diagnose a group of patients with a mental health condition based on text-based survey data. This classification was approached through several feature engineering and machine learning models to determine the optimal methods for diagnosis classification. A model was created that successfully classified diagnosis documents to their appropriate mental health condition, but due to limitation in the patient dataset, no model successfully classified patient diagnoses

Selection of Clusters based on Internal Indices in Multi-Clustering Collaborative Filtering Recommender System

The successful application of a multi-clustering based neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability.Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemesbased on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendationperformance, as it typically improves after the selection process.Furthermore, a smaller number of clustering schemes used asinput for the recommender system enhances scalability andreduces memory consumption. The findings are compared withbaseline recommenders’ outcomes to validate the effectiveness ofthe proposed approach

Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%

A Novel Combined Investment Recommender System Using Adaptive Neuro-Fuzzy Inference System [védés előtt]

Investment recommendation systems (IRSs) are critical tools used by potential investors to make informed decisions about investment options. However, existing systems have limitations in terms of accuracy and efficiency, leading to a need for more effective and efficient recommendation systems. This dissertation proposes the use of an adaptive neuro-fuzzy inference system (ANFIS) to develop a combined IRS that can provide accurate and efficient investment recommendations for potential investors. The main research question for this study is "How can an ANFIS be utilized to propose an effective and efficient investment recommendation system?" The specific sub-goals of the study are: 1) to categorize and cluster potential investors based on available data to make accurate investment recommendations, 2) to offer customized investment-type services using adaptive neural-fuzzy inference solutions for different categories of potential investors, and 3) to propose a combined recommender system to provide appropriate investment type recommendations for all categorized and clustered potential investors. The dissertation is structured into five chapters. Chapter I provides an overview of the research question and objectives, and Chapter II presents a theoretical framework and literature review, covering existing research on ANFIS in investment recommendation systems. Chapter III explains the methodology used to develop the combined IRS using ANFIS, including data collection, categorization and clustering of potential investors, development of the combined ANFIS model, and evaluation of the proposed system. Chapter IV presents the experimental results and analysis, highlighting the effectiveness of the model in providing appropriate investment-type recommendations for categorized and clustered potential investors. This chapter describes seven experiments that focused on investment recommender systems. Each experiment proposed a unique system that utilized various features of potential investors and their investment type experiences, in addition to employing fuzzy neural inference and the K-Means technique to generate personalized investment recommendations. The first experiment proposed a demographic ANFIS that utilized customer feedback and fuzzy neural inference to generate personalized investment recommendations. The second experiment proposed an automatic recommender system that worked with four key decision factors (KDFs) of potential investors: system value, environmental awareness, high return expectation, and low return expectation. The third experiment used potential investors' financial management traits and investment type for the recommendation. The model was based on an ANFIS, and feedback from knowledge experts and investors was used to improve the system. The fourth experiment used potential investors' experiences data to predict investment outcomes, and the system's performance was evaluated by comparing its recommendations with actual investment outcomes. The fifth experiment proposed an ANFIS-based investment recommendation system based on customers' financial situations, risk tolerance, and investment goals. The sixth experiment investigated the impact of personal characteristics such as age, income, and education level, as well as managerial issues, on investment decisions. The seventh experiment combined and clustered data from the six previous ANFIS systems to provide accurate investment recommendations. The system utilized clustering techniques to group customers with similar financial situations and investment goals, thereby enhancing the personalization of the recommendations. Overall, these experiments propose a novel approach to developing an effective and efficient investment recommendation system using ANFIS. The proposed system has the potential to significantly improve the accuracy and efficiency of investment recommendations, thereby enhancing the decision-making process for potential investors. A comparison of the results with other existing methods and a discussion of the limitations and challenges faced during the development of the system are also included in this chapter. Finally, Chapter V provides a comprehensive discussion of the research findings and their implications, including suggestions for future research. Overall, this dissertation proposes a novel approach to developing an effective and efficient investment recommendation system using ANFIS. The proposed system has the potential to significantly improve the accuracy and efficiency of investment recommendations, thereby enhancing the decision-making process for potential investors

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system