Semantic modelling of user interests based on cross-folksonomy analysis

The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combine

USING TAGS IN AN AIML-BASED CHATTERBOT TO IMPROVE ITS KNOWLEDGE

Nowadays, it is common to find on the Internet different conversational robots which interact with users simulating a natural language conversation. Among them, we can emphasize the chatterbots based on AIML language. In this paper we present an AIML based chatterbot that shows as its main contribution the use of tags and folksonomies. Thanks to its use, we can generate a context for each conversation, being able to maintain a state for each user in the system, and improving the adaptation capabilities of the bot

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Correlating user profiles from multiple folksonomies

As the popularity of the web increases, particularly the use of social networking sites and Web2.0 style sharing platforms, users are becoming increasingly connected, sharing more and more information, resources, and opinions. This vast array of information presents unique opportunities to harvest knowledge about user activities and interests through the exploitation of large-scale, complex systems. Communal tagging sites, and their respective folksonomies, are one example of such a complex system, providing huge amounts of information about users, spanning multiple domains of interest. However, the current Web infrastructure provides no mechanism for users to consolidate and exploit this information since it is spread over many desperate and unconnected resources. In this paper we compare user tag-clouds from multiple folksonomies to: (a) show how they tend to overlap, regardless of the focus of the folksonomy (b) demonstrate how this comparison helps finding and aligning the user's separate identities, and (c) show that cross-linking distributed user tag-clouds enriches users profiles. During this process, we find that significant user interests are often reflected in multiple Web2.0 profiles, even though they may operate over different domains. However, due to the free-form nature of tagging, some correlations are lost, a problem we address through the implementation and evaluation of a user tag filtering architecture

Similaridade entre perfis sociais

Mestrado em Sistemas de InformaçãoNa última década, a utilização da Internet tornou-se viral e de extrema importância posicionando-se, atualmente, numa parte integral das nossas vidas incluindo a parte social. As redes sociais online são uma das fontes mais ricas de informação sobre os perfis de utilizadores. Ao lidar com dados de redes sociais, a similaridade entre perfis representa uma área que tem tido algum destaque. Uma ferramenta capaz de identificar corretamente utilizadores semelhantes pode ser utilizada em diversas áreas e contribuir para tomadas de decisão importantes que poderão resultar em proveitos, sejam de cariz financeiro ou melhoria da qualidade de vida de pessoas (por exemplo, saúde). Nesta dissertação são realizados estudos com diferentes métricas de distância de forma a determinar a similaridade entre perfis. É possível, também, criar agrupamentos de perfis assim como correlacionar interesses. Posteriormente, é feita uma análise de performance entre diversos algoritmos de clustering, nomeadamente o K-Means, Clustering Hierárquico, DBSCAN e BIRCH. As medidas de similaridade foram também utilizadas para estimar valores associados aos interesses dos utilizadores, numa abordagem inspirada nos sistemas de recomendação.Over the last decade, Internet usage has gone viral and become extremely important, positioning itself as an integral part of our lives and social interactions. Social networks are now one of the richest sources of information regarding user profiles. By dealing with social network data, an area that as recently seen growing interest, one can create methods to study profile similarity, using available user data. A possible tool to correctly identify similar users may be applied in a multitude of areas and contribute to the decision making process that may draw gains to people’s lives, either in financial perspective or life quality (such as health). In this dissertation, metrics that are applied when determining profile similarity were researched and discussed, giving a complete overview on the concepts involved and difficulties experienced. Moreover, it is possible to create profile clusters as well as correlate interests. As such, a performance analysis of different clustering algorithms is done, namely K-Means, Hieratical Clustering, DBSCAN and BIRCH. Techniques used in recommendations systems are also discussed. Finally, future work is proposed where this project would serve as the basis of a recommendation and profile analysis systems

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

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

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