Learning browsing patterns for context-aware recommendation

The success of personal information agents depends on their capacity to both identify relevant information for users and proactively recommend context-relevant information. In this paper, we propose an approach to enable proactive context-aware recommendation based on the knowledge of both user interests and browsing patterns. The pro- posed approach analyzes the browsing behavior of users to derive a semantically enhanced context that points out the information which is likely to be relevant for a user according to its current activities.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI

An Application of Collaborative Web Browsing Based on Ontology Learning from User Activities on the Web

With explosively increasing amount of information on the Web, users have been getting more bored to seek relevant information. Several studies have introduced adaptive approaches to recognizing personal interests. This paper proposes the collaborative Web browsing system that can support users to share knowledge with other users. Especially, we have focused on user interests extracted from their own activities related to bookmarks. A simple URL based bookmark is provided with semantic and structural information by the conceptualization based on ontology. In order to deal with the dynamic usage of bookmarks, ontology learning based on a hierarchical clustering method can be exploited. As a result of our experiments, about 53.1 % of the total time was saved during collaborative browsing for seeking the equivalent set of information, compared with single Web browsing. Finally, we demonstrate implementing an application of collaborative browsing system through sharing bookmark-associated activities

Smart Conversational Agents for Reminiscence

In this paper we describe the requirements and early system design for a smart conversational agent that can assist older adults in the reminiscence process. The practice of reminiscence has well documented benefits for the mental, social and emotional well-being of older adults. However, the technology support, valuable in many different ways, is still limited in terms of need of co-located human presence, data collection capabilities, and ability to support sustained engagement, thus missing key opportunities to improve care practices, facilitate social interactions, and bring the reminiscence practice closer to those with less opportunities to engage in co-located sessions with a (trained) companion. We discuss conversational agents and cognitive services as the platform for building the next generation of reminiscence applications, and introduce the concept application of a smart reminiscence agent

Application of User Profiling on Ontology Module Extraction for Medical portals

One fit all for approach for searching and ranking discovered knowledge on the Internet does not cater for the diverse variety of users and user groups with different preferences, information needs and priorities. This is of a particular case in the National electronic Library of Infection in the UK (NeLI, www.neli.org.uk) accessed by a number of medical professionals with different preferences and medical information needs. We define personal and group profiles, based on user-specified interests, and develop an ontology module extraction service defining the key area of the infection ontology of a particular relevance to each user group. In this paper we discuss how ontology modularisation can improve the NeLI portal by providing customised alert, recommender service and specialitycustomised browsing tree structure

PhishDef: URL Names Say It All

Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by attackers. Second, we evaluate the classification accuracy when using only lexical features, both automatically and hand-selected, vs. when using additional features. We show that lexical features are sufficient for all practical purposes. Third, we thoroughly compare several classification algorithms, and we propose to use an online method (AROW) that is able to overcome noisy training data. Based on the insights gained from our analysis, we propose PhishDef, a phishing detection system that uses only URL names and combines the above three elements. PhishDef is a highly accurate method (when compared to state-of-the-art approaches over real datasets), lightweight (thus appropriate for online and client-side deployment), proactive (based on online classification rather than blacklists), and resilient to training data inaccuracies (thus enabling the use of large noisy training data).Comment: 9 pages, submitted to IEEE INFOCOM 201

The fatality index of public transport (express bus) in Malaysia

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Learning browsing patterns for context-aware recommendation

The success of personal information agents depends on their capacity to both identify relevant information for users and proactively recommend context-relevant information. In this paper, we propose an approach to enable proactive context-aware recommendation based on the knowledge of both user interests and browsing patterns. The pro- posed approach analyzes the browsing behavior of users to derive a semantically enhanced context that points out the information which is likely to be relevant for a user according to its current activities.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI