Collaborative information seeking with ant colony ranking in real-time

In this paper we propose a new ranking algorithm based on Swarm Intelligence, more specifically on the Ant Colony Optimization technique, to improve search engines’ performances and reduce the information overload by exploiting users’ collective behavior. We designed an online evaluation involving end users to test our algorithm in a real-world scenario dealing with informational queries. The development of a fully working prototype – based on the Wikipedia search engine – demonstrated promising preliminary results

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

Context-awareness for adaptive information retrieval systems

Philosophiae Doctor - PhDThis research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectivenes

Bot recognition in a Web store: An approach based on unsupervised learning

Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning strategy that decouples modelling the data from labelling them. Its efficiency is evaluated through experiments on real e-commerce data, in realistic conditions, and compared to that of supervised learning classifiers (a multi-layer perceptron neural network and a support vector machine). Results demonstrate that the classification based on unsupervised learning is very efficient, achieving a similar performance level as the fully supervised classification. This is an experimental indication that the bot recognition problem can be successfully dealt with using methods that are less sensitive to mislabelled data or missing labels. A very small fraction of sessions remain misclassified in both cases, so an in-depth analysis of misclassified samples was also performed. This analysis exposed the superiority of the proposed approach which was able to correctly recognize more bots, in fact, and identified more camouflaged agents, that had been erroneously labelled as humans

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

Advanced Web User Monitoring with Real-Time Communications Devices

This paper presents a new approach to web user monitoring with real-time communications devices. Proposed solution is based on a combination of WebRTC technology, performance data logging and third party services. This solutions allows measurement of a user’s web performance data, evaluated user’s behavior and makes gender and age classification. The results are an important tool in understanding what people think and feel while browsing a website. Thanks to this expertise we can better tailor web page content to achieve our business goals. Experimental results published in this paper were conducted on real data and show that the proposed solution of capturing and transforming face images from a video stream allows achievement of very high accuracy of gender and age classification by third party services

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching