Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Study of website Promotion Techniques and Role of SEO in search engine results

The explosion in the designing of websites to reach maximum people by business sectors has been tremendously increased in last few years. Obviously website is of no use if it not visited. There are number of ways to promote the website and reach to maximum users. To promote website through search engine results is most effective way. Promoting website in search engine result has been completed with Search Engine Optimization (SEO) techniques. It is possible to move a web page to the top list results of a search engine by using only some necessary optimization rules. SEO is helping a web site to appear in first result page of a search engine. SEO is least costly and most effective way to reach many people. In the present paper we explained different website promotion techniques, explores the different search engine optimization techniques with on-page and off-page optimization. This paper reviews the work done by different researcher which identify on-page optimization techniques used in web pages using different methods and among them find out important on-page optimization techniques to help website to rank high in search engine results

Media masters and grassroot art 2.0 on Youtube

Communication in the Web 2.0 context mainly works through images. The online video platform YouTube uses this form of visual communication and makes art forms of Western societies visible through their online videos. YouTube, as cultural reservoir and visual archive of moving images, accommodates the whole range of visualising creative processes – from artistic finger exercises to fine arts. A general characteristic of YouTube is the publishing of small everyday gestures of the ‘big ones’ (politicians, stars), like small incidents and their clumsiness in everyday actions, e.g. Beyonce´s fall from the stage or Tom Cruise’s demonic pro-scientology interview. Through their viral distribution on different platforms, these incidents will never be covered up or disappear from the public view. At the same time big gestures and star images are replicated and sometimes reinterpreted by the ‘small people’ who present themselves in the poses and attitudes of the stars. Generally, a coexistence of different perspectives is possible. YouTube allows polysemic and polyvalent views on the everyday and media phenomena. This article relies on YouTube research 2 that started in 2006 at the New Media Department of the Goethe University of Frankfurt. The results of the research have already presented representative forms and basic patterns, that is to say, categories for the clips appearing here. These kinds of clips, recurring in the observation period, have an impact on the basic representation of art or artistic expression within moving images on this platform. Methodologically the focus leads to the investigation (which has to be adequate to the specifics of the medium, or ‘media adequate’) of new visual structures and forms which can create – consciously or unconsciously – an art form. After focusing on the media structures, it will be discussed whether any and, if so, which ‘authentic’ new forms were developed solely on YouTube and whether these forms are innovative and can be characterised as avant-garde. This article first takes a small step in evaluating how to get from a general communication through means of visuality in web 2.0, an often endless chatty cheesy visual noise 3 – to the special quality of a consciously created aesthetic. From where do innovative aesthetic forms emerge, related to their media structures? 4 Are they the products of ‘media amateurs’ 5 or do we have to find new specifications and descriptions for the producers? The definition of a ‘media amateur’ describes technically interested private individuals who acquire and develop technology before commercial use of the technology is even recognisable. Just as artists are developing their own techniques, according to Dieter Daniels, media amateurs are autodidacts who invent techniques, rather than just acquire knowledge about them (see for example the demo scene, the machinima, brickfilm producers as well as many areas of computer gaming in general 6). The media amateur directly intervenes in the production processes of the medium and does not just simply use the medium. What is fascinating is the media amateur’s process of self education – not the result – and the direct impact on the internal structure and the control of the medium. 7 Media amateurs open a previously culturally unformed space of experience. This only partially applies to most of the YouTube clips in the realms of the visual arts; it is here most important to look at the visual content. This article discusses all these concepts and introduces new descriptions for the different forms of production: the technically oriented media master, the do-it-yourselfer, the tinkerer, the amateur handicraftsman and the inventor. It outlines a basic research project on ‘visual media culture’ (a triangulation of research on media structure and iconography) of the presented online video platform. It is a product of the analysis of clips focusing on the media structure, analyzing the creative handling of images and the deviations and differences of pre-set media formats and stereotypes

Search Engine Optimization Algorithms for Page Ranking: Comparative Study

Every second, the number of visitors increase day by day due to the fast growing of World Wide Web. Till this day there are more than 11.3 billion web pages in the World Wide Web. In the modern era of technology and advance computation, world page ranking become a common feature of modern retrieval system. However, any query in search engine will display both relevant and irrelevant data that can cause overhead to the search engine and will affect the page ranking process. A new optimization technique is needed to improve the existing search engine optimization in increasing the page ranking. This paper presents a comparative study of different page ranking algorithms for search engine optimization. Also it explores some improvements that are needed to overcome the current problem in this field. The simulation result’s analysis clearly shows that there is a need of new optimization technique. This new technique must reduce the complexity and user overhead by displaying only related data which will reduce overheading in search engine

Semantic contextualisation of social tag-based profiles and item recommendations

Proceedigns of 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23014-1_9We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Community of Madrid (CCG10- UAM/TIC-5877

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook