Final report, independent Study during Fall 2009 "Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles"

This report describes our study of different ways to improve existing collaborative filtering techniques in order to recommend scientific articles. Using data crawled from CiteUlike, a collaborative tagging service for academic purposes, we compared the classical user-based collaborative filtering algorithm as described by Schafer et al. [2], with two enhanced variations: 1) using a tag-based similarity calculation, to avoid depending on ratings to find the neighborhood of a user, and 2) incorporate the amount of raters in the final recommendation ranking to decrease the noise of items that have been rated by too few users. We provide a discussion of our results, describing the dataset and highlighting our findings about applying collaborative filtering on folksonomies instead of the classic bipartite user-item network, and providing guidelines of our future research

The Computational Approach for Recommendation System Based on Tagging Data

Recommendation approaches like a platform for learning algorithm. We can use some predicted values to put them in the data pipeline forlearning. There is a hard nuance of how to calculate the similarity measurewhen we have a small number of actions at all, its not a new user or item to use cold start methods, we just have not enough quantity to say it may be interpreted like regularity. The frequency of tags what we would have fromusers will have a huge impact to predict his future taste. The article describes created a computational approach using as explicit and as implicit feedbacks from users and evaluates tags by Jaccard distance to resolve this issue. To compare results with existed numerical methods there is a comparison table that shows the high quality of the proposed approach

#Socialtagging: Defining its Role in the Academic Library

The information environment is rapidly changing, affecting the ways in which information is organized and accessed. User needs and expectations have also changed due to the overwhelming influence of Web 2.0 tools. Conventional information systems no longer support evolving user needs. Based on current research, we explore a method that integrates the structure of controlled languages with the flexibility and adaptability of social tagging. This article discusses the current research and usage of social tagging and Web 2.0 applications within the academic library. Types of tags, the semiotics of tagging and its influence on indexing are covered

Intelligent recommendation system for e-learning platforms

As more and more digital resources are available, finding the appropriate document becomes harder. Thus, a new kind of tools, able to recommend the more appropriated resources according the user needs, becomes even more necessary. The current project implements an intelligent recommendation system for elearning platforms. The recommendations are based on one hand, the performance of the user during the training process and on the other hand, the requests made by the user in the form of search queries. All information necessary for decision-making process of recommendation will be represented in the user model. This model will be updated throughout the target user interaction with the platform

cTag: Semantic Contextualisation of Social Tags

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of the Workshop on Semantic Adaptive Social Web 2011In this paper, we present an algorithmic framework to identify the semantic meanings and contexts of social tags within a particular folksonomy, and exploit them for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Regional Government of Madrid (S2009TIC- 1542)

Folks in Folksonomies: Social Link Prediction from Shared Metadata

Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852

COLLABORATIVE TAGGING USING CAPTCHA

Tagging is most widely used feature in online networks. There are no of tags are available mainly offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Recently there is a problem based on the tagging of feedback, free-text labels etc. Without user permission tags are automatically generated spam scripts. So, users are facing many sensitive problems like privacy. In the existing system, a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Some problems identified in the existing system. To overcome these problems captcha based security in introduced in the proposed system to provide better security for the tagging information. Results will show the performance of the proposed system