Collaborative Filtering Similarity Algorithm Using Common Items

Collaborative filtering (CF) plays an important role in reducing information overload by providing personalized services. CF is widely applied, but common items are not taken account in the similarity algorithm, which reduces the recommendation effect. To address this issue, we propose several methods to improve the similarity algorithm by considering common items, and apply the proposed methods to CF recommender systems. Experiments show that our methods demonstrate significant improvements over traditional CF

Supporting personalised content management in smart health information portals

Information portals are seen as an appropriate platform for personalised healthcare and wellbeing information provision. Efficient content management is a core capability of a successful smart health information portal (SHIP) and domain expertise is a vital input to content management when it comes to matching user profiles with the appropriate resources. The rate of generation of new health-related content far exceeds the numbers that can be manually examined by domain experts for relevance to a specific topic and audience. In this paper we investigate automated content discovery as a plausible solution to this shortcoming that capitalises on the existing database of expert-endorsed content as an implicit store of knowledge to guide such a solution. We propose a novel content discovery technique based on a text analytics approach that utilises an existing content repository to acquire new and relevant content. We also highlight the contribution of this technique towards realisation of smart content management for SHIPs.<br /

GUMCARS: General User Model for Context-Aware Recommender Systems

Context-Aware Recommender Systems (CARS) are extensions of traditional recommender systems that use information about the context of the user to improve the recommendation accuracy. Whatever the specific algorithm exploited by the CARS, it can provide high-quality recommendations only after having modeled the user and context aspects. Despite the importance of the data models in CARS, nowadays there is a lack of models and tools to support the modeling and management of the data when developing a new CARS, leaving designers, developers and researchers the work of creating their own models, which can be a hard and time-consuming labor, and often resulting in overspecialized or incomplete models. In this paper, we describe GUMCARS - a General User Model for Context-Aware Recommender Systems, where the main goal is to help designers and researchers when creating a CARS by providing an extensive set of User, Context and Item aspects that covers the information needed by different recommendation domains. To validate GUMCARS, two experiments are performed; first, the completeness and generality of the model are evaluated showing encouraging results as the proposal was able to support most of the information loaded from real-world datasets. Then the structural correctness of the model is assessed, the obtained results strongly suggest that the model is correctly constructed according to Object-Oriented design paradigm

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

Patterns of Emoji Use for Individual Twitter Users: An Exploratory Analysis

This paper provides a new perspective of looking at emoji: Users. On 100 Twitter users represented by emojis from 200 tweets, an exploratory analysis is conducted to find patterns of emoji use for individual users. We use k-means clustering, principal component analysis and hierarchical clustering on different distance measures, with special focus on outlying users with unique using patterns. Our findings could give insights of how the ways people use emoji converge and diverge, show hidden connections between emojis, and help people better understand this novel language in the digital era.Master of Science in Information Scienc

Personalized question-based cybersecurity recommendation systems

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts

Opinion Detection, Sentiment Analysis and User Attribute Detection from Online Text Data

With the growing increase in the use of the internet in most parts of the world today, users generate significant amounts of online text on different platforms such as online social networks, product review websites, travel blogs, to name just a few. The variety of content on these platforms has made them an important resource for researchers to gauge user activity, determine their opinions and analyze their behavior, without having to perform monetarily and temporally expensive surveys. Gaining insights into user behavior enables us to better understand their likes and dislikes, which in turn is helpful for economic purposes such as marketing, advertising and recommendations. Further, owing to the fact that online social networks have recently been instrumental in socio-political revolutions such as the Arab Spring, and for awareness-generation campaigns by MoveOn.org and Avaaz.org, analysis of online data can uncover user preferences. The overarching goal of this Ph.D. thesis is to pose some research questions and propose solutions, mostly pertaining to user opinions and attributes, keeping in mind the large quantities of noise present in online textual data. This thesis illustrates that with the extraction of informative textual features and the use of robust NLP and machine learning techniques, it is possible to perform efficient signal extraction from online text data, and use it to better understand user behavior. The first research problem addressed is that of opinion detection and sentiment analysis of users on a given topic, from their self-generated tweets. The key idea is to select relevant hashtags and n-grams using an $l_1$-regularized logistic regression model for opinion detection. The second research problem deals with temporal opinion detection from tweets, i.e., detecting user opinions on a topic in which the conversation evolves over time. For instance, on the widely-discussed topic of Obamacare (the Affordable Care Act in the U.S.), various issues became the focal points of discussion among users over time, as corresponding socio-political events and occurrences took place in real-time. We propose a machine-learning model based on seminal work from the sociological literature that is based on the premise that most opinion changes occur slowly over time. Our model is able to successfully capture opinions over time using publicly available tweets, as well as to uncover the key points of discussion as time progresses. In the third research problem, we utilize distributed representation of words in a method that determines, from user reviews, aspects of products and services that users like and dislike. We harness the contextual similarity between words and effectively build meta-features that capture user sentiment at a granular level. Finally in the fourth research problem, we propose a method to detect the age of users from their publicly available tweets. Using a method based on distributed representation of words and clustering, we are able to achieve high accuracies in age detection, as well as to simultaneously discover topics of conversation in which users of different age groups engage

A framework for understanding and predicting the take up and use of social networking tools in a collaborative envionment

Online collaborative environments, such as social networking environments, enable users to work together to create, modify, and share media collaboratively. However, as users can be autonomous in their actions the ability to create and form a shared understanding of the people, purpose, and process of the collaborative effort can be complex. This complexity is compounded by the natural implicit social and collaborative structure of these environments, a structure that can be modified by users dynamically and asynchronously. Some have tried to make this implicitness explicit through data mining, and allocation of user roles. However such methods can fail to adapt to the changing nature of an environment's structure relating to habits of users and their social connectedness. As a result, existing methods generally provide only a snapshot of the environment at a point in time. In addition, existing methods focus on whole user bases and the underlying social context of the environment. This makes them unsuitable for situations where the context of collaboration can change rapidly, for example the tools and widgets available for collaborative action and the users available for collaborative interactions. There is a pre-existing model for understanding the dynamic structure of these environments called the “Group Socialisation Model". This model has been used to understand how social group roles form and change over time as they go through a life cycle. This model also contains a concept of characteristic behaviours or descriptors of behaviour that an individual can use to make judgement about another individual and to create an understanding of a role or social norm that may or may not be explicit. Although studies have used components of this model to provide a means of role identification or role composition within online collaborative environments, they have not managed to provide a higher level method or framework that can replicate the entire life cycle continuously over time within these environments. Using the constructive research methodology this thesis presents a research construct in the form of a framework for replicating the social group role life cycle within online collaborative environments. The framework uses an artificial neural network with a unique capability of taking snapshots of its network structure. In conjunction with fuzzy logic inference, collaborative role signatures composed of characteristic behaviours can then be determined. In this work, three characteristic behaviours were identified from the literature for characterisation of stereotypical online behaviour to be used within a role signature: these were publisher, annotator, and lurker. The use of the framework was demonstrated on three case studies. Two of the case studies were custom built mobile applications specifically for this study, and one was the Walk 2.0 website from a National Health and Medical Research Council project. All three case studies allowed for collaborative actions where users could interact with each other to create an dynamic and diverse environment. For the use of these case studies, ethics was approved by the Western Sydney University Human Research Ethic Committee and consistent strategies for recruitment were carried out. The framework was thereby demonstrated to be capable of successfully determining role signatures composed of the above characteristic behaviours, for a range of contexts and individual users. Also, comparison of participant usage of case studies was carried out and it was established that the role signatures determined by the framework matched usage. In addition, the top contributors within the case studies were analysed to demonstrate the framework's capability of handling the dynamic and continual changing structure of an online collaborative environment. The major contribution of this thesis is a framework construct developed to propose and demonstrate a new framework approach to successfully automate and carry out the social group role model life cycle within online collaborative environments. This is a significant component of foundational work towards providing designers of online collaborative environments with the capacity of understanding the various implicit roles and their characteristic behaviours for individual users. Such a capability could enable more specific individual personalisation or resource allocation, which could in turn improve the suitability of environments developed for collaboration online

The use of recommender and decision support systems for sales personalization in a mobile application

In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs. By defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales. This information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations. With the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns. On the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions

The use of recommender and decision support systems for sales personalization in a mobile application

In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs. By defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales. This information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations. With the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns. On the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions