FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

On User Modelling for Personalised News Video Recommendation

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

Linking Disability and Intercultural Studies: the adaptation journey of the visually impaired migrant in Ireland

This study focuses on the lived experiences of the visually impaired migrant in Ireland and this is the first study to document the lives of these members of Irish society. It examines how visually impaired migrants are simultaneously adapting to their disability and a new cultural environment while living in Ireland. In so doing this study aims to link the two academic fields of Intercultural Studies and Disability Studies and theoretical underpinnings for this study are drawn and woven together from both fields. As such this study draws from the development of theories relating to disability as well as the intercultural aspects of migration. Qualitative in-depth semi-structured interviews were conducted with 22 participants living in the larger Dublin region, which comprised of two groups; migrant users and providers of services for the visually impaired. Data analysis was assisted through the software package Atlas.ti. A grounded theory approach to collecting and analysing data was adopted as this facilitates the flow from raw data to codes to concepts. Purposive sampling was employed and the typical method of grounded theory of constant comparison was not used, rather interviews were analysed individually once they were all completed then compared. Research findings indicate that the cultural perceptions of disability may help or hinder the individual’s adaptation process both to their visual impairment and to living and integrating into a new culture in Ireland. Findings cluster around the three areas of cultural perceptions of disability, support networks and cultural barriers to adaptation. Synergising theoretical concepts and data steered the development of a new integrative model which identifies the inhibitors and facilitators for the process of adaptation to visual impairment for a migrant in Ireland

Subject-relevant Document Recommendation: A Reference Topic-Based Approach

Knowledge-intensive workers, such as academic researchers, medical professionals or patent engineers, have a demanding need of searching information relevant to their work. Content-based recommender system (CBRS) makes recommendation by analyzing similarity of textual contents between documents and users’ preferences. Although content-based filtering has been one of the promising approaches to document recommendations, it encounters the over-specialization problem. CBRS tends to recommend documents that are similar to what have been in user’s preference profile. Rationally, citations in an article represent the intellectual/affective balance of the individual interpretation in time and domain understanding. A cited article shall be associated with and may reflect the subject domain of its citing articles. Our study addresses the over-specialization problem to support the information needs of researchers. We propose a Reference Topic-based Document Recommendation (RTDR) technique, which exploits the citation information of a focal user’s preferred documents and thereby recommends documents that are relevant to the subject domain of his or her preference. Our primary evaluation results suggest the outperformance of the proposed RTDR to the benchmarks

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