Considering correlation retarded growth for personalized recommendation in social tagging

Due to the massive amounts of data, finding social media suited to their need is a challenging issue. To help such users retrieve useful social media content, we propose a new model of personalized recommendation system by using annotating information from relationship among users, tags, and items. However, the frequency of users’ tagging has strong or weak correlation, which affects the dynamic interest mining of users. In this paper, CRGI is proposed to describe the correlation between users and tags or tags and items. Our approach has two phases, in the first phase, we describe the correlation between users, items and tags by CRGI and in the second phase, we build a tag-item weight model and a user-tag preference model on the basis of the first phase. Then we utilize the two models to find the suitable items with the highest scores. The experimental results demonstrate that the item recommendation performance is improved in both the accuracy and the diversity, and validate that the proposed personalized approach is effective for improving the social media recommendation

Multi-dimensional clustering in user profiling

User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Wiki semántica usando Folksonomies

Una Wiki Semántica posee las mismas características básicas de una Wiki tradicional pero incorporando al lenguaje de markup meta información para darle significado, esta meta información es comprensible por una máquina. En una Wiki semántica las páginas son anotadas con información semántica lo cual permite una mejor obtención de la información, mejorar las búsquedas y emerger representaciones de conocimiento. Esta incorporación de elementos semánticos incorpora nuevos desafíos en el desarrollo de Wikis Semánticas: por un lado preservar la simplicidad de gestar información y por el otro permitir a los usuarios manejar anotaciones semánticas para darle un mayor gerenciamiento del conocimiento que emerge de la comunidad que se genera alrededor de la Wiki. La información semántica representa la base de conocimiento compartida, la cual describe el entendimiento común entre los participantes de la comunidad wiki. La creación colaborativa de esta base de conocimiento se realiza a través de un proceso iterativo y social.Facultad de Informátic

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology

A Study of User Profile Generation from Folksonomies

Recommendation systems which aim at providing relevant information to users are becoming more and more important and desirable due to the enormous amount of information available on the Web. Crucial to the performance of a recommendation system is the accuracy of the user profiles used to represent the interests of the users. In recent years, popular collaborative tagging systems such as del.icio.us have aggregated an abundant amount of user-contributed metadata which provides valuable information about the interests of the users. In this paper, we present our analysis on the personal data in folksonomies, and investigate how accurate user profiles can be generated from this data. We reveal that the majority of users possess multiple interests, and propose an algorithm to generate user profiles which can accurately represent these multiple interests. We also discuss how these user profiles can be used for recommending Web pages and organising personal data

Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

Vuorikari, R. (2009). Tags and self-organisation: a metadata ecology for learning resources in a multilingual context. Doctoral thesis. November, 13, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC.This thesis studies social tagging of learning resources in a multilingual context. Social tagging and its end products, tags, are regarded as part of the learning resources metadata ecology. The term “metadata ecology” is used to mean the interrelation of conventional metadata and social tags, and their interaction with the environment, which can be understood as the repository in the large sense (resources, metadata, interfaces and underlying technology) and its community of users. The main hypothesis is that the self-organisation aspect of a social tagging system on a learning resource portal helps users discover learning resources more efficiently. Moreover, user-generated tags make the system, which operates in a multilingual context, more robust and flexible. Social tags offer an interesting aspect to study learning resources, its metadata and how users interact with them in a multilingual context. Tags, as opposed to conventional metadata description such as Learning Object Metadata (LOM), are free, non-hierarchical keywords that end-users associate with a digital artefact, e.g. a learning resource. Tags are formed by a triple of (user,item,tag). Tags and the resulting networks, folksonomies, are commonly modelled as tri- partite hypergraphs. This ternary relational structure gives rise to a number of novel relations to better understand, capture and model contextual information. This thesis first provides two exploratory studies to better understand how users tag learning resources in a multilingual context and to find evidence on the “cross-boundary use” of learning resources. The term cross-boundary use means that the user and the resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. The second part introduces a trilogy of studies focusing on self-organisation, flexibility and robustness of a social tagging system using empirical, behavioural data captured from log-files and user’s attention metadata trails on a number of learning resource portals and platforms in a multilingual context