Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems

With the prosperity and popularity of social tagging communities, developing an enormous amount of user-generated data with modalities has emerged in recent years. Personalized search based on tag-based profiles, an indispensable and prominent way to assist users to access and retrieve their interested resources, has been extensively studied by research communities. In this paper, we revisit the extant profiling approaches to personalized search in collaborative tagging systems. Specifically, we attempt to answer the following research questions: (i) how many tags are sufficient for user and resource profiling? and (ii) under what circumstances should profile enriching/refining techniques be used to promote the effectiveness of personalized search? The result of our experimental studies in a real-world dataset indicate that the rational size of tags for constructing user/resource profiles does exist. This size can also provide us with an insight into when profiles should be enriched or refined for active/inactive users and resources. We believe the findings of this paper can be quite useful for the future applications in social tagging systems such as user interest predictions or resource recommendations.

An ever increasing number of Web sites that allow users to easily collaborate, produce, and share content and interact have turned the Web into a more dynamic social place often referred to as Social Web. One form of Social Web sites is collaborative tagging systems that allow their users to annotate Web resources using tags, thus intuitively organizing them and making them easily findable. Tags would have been much more useful if collaborative tagging systems had been collaborating and allowing for integration of their tagging metadata. In this paper we address this issue of lack of collaboration by suggesting an approach for achieving that collaboration. We also present a concrete system called TagFusion that we developed to test the feasibility of the suggested approach. Special attention has been given to the strategies for attracting collaborative tagging systems to integrate using TagFusion, as well as to the possibility of involving artificial entities in the annotation process in accordance with the Semantic Web vision.

In recent years, the B2C e-commerce achieved a rapid development on a global scope; more and more people began to use the Internet for shopping. However, the exponentially increasing information provided by Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such problem is to build personalized recommender systems to retrieve product information that really interests the customers. The rapid development of Web 2.0 provides new ideas for personalized recommendation. In this paper we introduce the collaborative filtering, knowledge-based approaches and hybrid approaches in building recommender systems and discuss the strengths and weaknesses of each approach, we propose a collaborative tagging system to provide personalized product information to customers in B2C e-commerce websites and describe the system's architecture and point the system's advantage.

Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages.In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system.

PurposeThe purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies.Design/methodology/approachA selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word‐matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree.FindingsThe experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two‐thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian‐pattern.Research limitations/implicationsThe results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies.Practical implicationsThe linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases.Originality/valueThis is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority‐maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.

Ontologies define a set of terms and the relationships (e.g., is-a and has-a) between them; they are the building block of the emerging semantic web. An ontology relating the tags in a collaborative tagging system (CTS) makes the CTS easier to understand. We propose an algorithm to automatically construct an ontology from CTS data and conduct a detailed empirical comparison with previous related work on four real data sets – Del.icio.us, LibraryThing, CiteULike, and IMDb. We also verify the effectiveness of our algorithm in detecting is-a and has-a relationships.Keywordsontologytaxonomytagcollaborative tagging systems

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high‐quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph‐based algorithm, SPEAR (spamming‐resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext‐induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems.In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relations in social tagging system. Our methods noticeably outperform state-of-the-art approaches.

We consider search engines and collaborative tagging systems from the perspective of resource discovery and re-finding on the Web. We performed repeated searches over nine-months on Google and del.icio.us for web pages related to three topics selected to have different dynamic characteristics. The results show differences in the resources they provide to the searcher. The resources tagged on del.icio.us differ strongly from the top results returned by Google. The results also suggest the changes in the most recently tagged web pages may be associated with the level of activity in user communities and, indirectly, with external events.

Collaborative Tagging is the process of managing, classifying and describing tags where in user can easily describe or get an information about an item. As growth of information in social networking websites there is a possibility of increase of tags and it is very difficult to predict or recommend items to the user. In this paper we address the problem of sensitivity in the distribution of the tag data in clusters and optimization can be achieved by merging similar clusters in E-FCM and E-GK algorithm for collaborative Tagging Systems. Our experiments on each movie data set shows E-FCM and E-GK with sensitivity gives more accurate results as compared to baseline clustering approaches in collaborative tagging systems.

Collaborative tagging is a popular method in social resource sharing system. The reason why collaborative tagging system becomes popular is users can freely tags on the resources, like Flickr1 and Delicious2 . Any relationship between tags and resources are not made explicitly when tagging in folksonomy. Due to lack of semantic meaning, it is difficult for users to find related resources. Also, short of sufficient number of relevant tags increase the data sparsity which decreases the rate of information extraction against the user queries. Defining semantic relationship between tags, resources and users is an important research issue to retrieval of related information from folksonomies. In this paper, we propose a WordNet based method to find the semantic relationship between tags in folksonomy. Finally, the experiment results using Flickr data show our proposed method works well finding semantic relationship between tags in folksonomy.

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Los sistemas de recomendación han tenido un rápido crecimiento tanto en investigación académica como en aplicaciones industriales en los últimos años. Las recomendaciones se nos proporcionan cuando elegimos elementos como música, películas, restaurantes, noticias, etc. Como parte de las técnicas de recomendación más prometedoras, los sistemas basados en recomendación de filtros colaborativos desempeñan un papel importante en las aplicaciones actuales. La aparición de nuevas fuentes de información requiere la combinación de sistemas de recomendación con técnicas avanzadas de otros campos, como el análisis de redes sociales, el procesamiento del lenguaje natural, el aprendizaje en profundidad, etc. Esta combinación mejora la calidad de las recomendaciones. Los sistemas de recomendación anteriores se centran principalmente en las mejoras de la precisión de la recomendación. Recientemente, surge una tendencia para la evaluación del rendimiento de los sistemas de recomendación que toma muchas propiedades nuevas en consideración. La fiabilidad es uno de los aspectos más notables y será profundamente estudiado en esta tesis. Se han propuesto algunas medidas para medir la fiabilidad de los sistemas de recomendación de filtrado colaborativo basados en la memoria. Sin embargo, observamos un área sin cubrir en el diseño de las medidas de fiabilidad adecuadas para los sistemas de recomendación de filtros colaborativos basados en modelos, especialmente aquellos basados en la factorización matricial. También encontramos que hay una falta de medidas de calidad de fiabilidad adecuadas. Esta tesis se concentra en proporcionar soluciones a ambas ausencias. Después de un estudio exhaustivo de trabajos de investigación del estado del arte, proponemos una nueva medida de fiabilidad que supera los desafíos de generalización y abstracción causados por los factores latentes de los métodos de factorización matricial. La medida propuesta se compara con con métodos y algoritmos representativos, usando bases de datos publicas, tanto académicas como comerciales. Los resultados experimentales muestran que nuestra nueva medida logra mejoras en la calidad de la recomendación. Además, la medida de fiabilidad propuesta también tiene un impacto muy positivo contra los "Shilling Attacks". Para obtener una mejora respecto a las medidas existentes de calidad de la fiabilidad, también proponemos, hasta donde sabemos, la primera medida de calidad de fiabilidad universal que es capaz de proporcionar valores numéricos para cualquier tipo de sistema de recomendación. Esta medida también se ha probado usando conjuntos de datos públicos reales y ha sido comparada con los métodos de calidad de la fiabilidad existentes. Basándonos en los resultados de los experimentos, concluimos que nuestra medida supera los últimos trabajos de investigación, proporcionando valores de calidad de fiabilidad más precisos e informativos. ----------ABSTRACT---------- Recommender systems have experienced a rapid growth in both academical research and industrial applications in latest years. Recommendations are provided to us when we choose items such as music, movies, restaurants, news, etc. As the most promising recommendation techniques, collaborative filtering recommender systems play an important role in current applications. The emergence of novel information sources requires to combine recommender systems with several advanced techniques coming from other fields like social network analysis, natural language processing, deep learning etc. This combination improves the quality of recommendations. Previous recommender systems research mainly focuses on the improvements of recommendation accuracy. Recently a new trend arises for the performance evaluation of recommender systems that takes many novel properties into consideration. Reliability is one of the most remarkable aspects, which will be deeply studied in this thesis. Some measures have been proposed for measuring reliability of memory based collaborative filtering recommender systems. However, we observe a gap in the design of proper reliability measures for model based collaborative filtering recommender systems, especially those based on matrix factorization. We also found that there is a lack in proper reliability quality measures. This thesis concentrates on providing solutions to both lacks. After a comprehensive study of state-of-the-art research works, we propose a novel reliability measure that overcomes the challenges of generality and abstraction caused by latent factors of matrix factorization methods. The proposed measure is tested against current baselines on public academic and commercial datasets; experiment results show that our novel measure achieves better improvements on recommendation quality. What’s more, the proposed reliability measure also has a very positive impact against shilling attacks. In order to fill in the blank of reliability quality measures, we also propose, as far as we know, the first universal reliability quality measure that is able to provide numerical values for any type of recommender systems. This measure has also been tested on public academic and commercial datasets using current methods. Based on the experiment results we conclude that our quality measure outperforms state-of-the-art research works by providing more accurate and informative reliability quality values.

Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users’ preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.