On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p

Study about the different use of explicit and implicit tags in social bookmarking

This is the accepted version of the following article: Arolas, E. E., & Ladrón-de-Guevar, F. G. (2012). Uses of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology, 63(2), 313-322. doi:10.1002/asi.21663, which has been published in final form at http://dx.doi.org/10.1002/asi.21663Although Web 2.0 contains many tools with different functionalities, they all share a common social nature. One tool in particular, social bookmarking systems (SBSs), allows users to store and share links to different types of resources, i.e., websites, videos, images. To identify and classify these resources so that they can be retrieved and shared, fragments of text are used. These fragments of text, usually words, are called tags. A tag that is found on the inside of a resource text is referred to as an obvious or explicit tag. There are also nonobvious or implicit tags, which don't appear in the resource text. The purpose of this article is to describe the present situation of the SBSs tool and then to also determine the principal features of and how to use explicit tags. It will be taken into special consideration which HTML tags with explicit tags are used more frequently.Estelles Arolas, E.; González Ladrón De Guevara, FR. (2012). Study about the different use of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology. 63(2):313-322. doi:10.1002/asi.21663S313322632Bar-Ilan, J., Zhitomirsky-Geffet, M., Miller, Y., & Shoham, S. (2010). The effects of background information and social interaction on image tagging. Journal of the American Society for Information Science and Technology, 61(5), 940-951. doi:10.1002/asi.21306Bateman, S., Muller, M. J., & Freyne, J. (2009). Personalized retrieval in social bookmarking. Proceedinfs of the ACM 2009 international conference on Supporting group work - GROUP ’09. doi:10.1145/1531674.1531688Delicious' Blog 2010 What's next for Delicious http://blog.delicious.com/blog/2010/12/whats-next-for-delicious.htmlDing, Y., Jacob, E. K., Zhang, Z., Foo, S., Yan, E., George, N. L., & Guo, L. (2009). Perspectives on social tagging. Journal of the American Society for Information Science and Technology, 60(12), 2388-2401. doi:10.1002/asi.21190Eisterlehner , F. Hotho , A. Jäschke , R. ECML PKDD Discovery Challenge 2009 (DC09)Farooq, U., Kannampallil, T. G., Song, Y., Ganoe, C. H., Carroll, J. M., & Giles, L. (2007). Evaluating tagging behavior in social bookmarking systems. Proceedings of the 2007 international ACM conference on Conference on supporting group work - GROUP ’07. doi:10.1145/1316624.1316677Farooq , U. Zhang , S.M. Carroll , J. 2009 Sensemaking of scholarly literature through taggingFu, W.-T., Kannampallil, T., Kang, R., & He, J. (2010). Semantic imitation in social tagging. ACM Transactions on Computer-Human Interaction, 17(3), 1-37. doi:10.1145/1806923.1806926Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964-971. doi:10.1145/32206.32212Golder , S.A. Huberman , B.A. 2005 The structure of collaborative tagging systems http://www.hpl.hp.com/research/idl/papers/tagsKörner, C., Benz, D., Hotho, A., Strohmaier, M., & Stumme, G. (2010). Stop thinking, start tagging. Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772744Koutrika, G., Effendi, F. A., Gyöngyi, Z., Heymann, P., & Garcia-Molina, H. (2008). Combating spam in tagging systems. ACM Transactions on the Web, 2(4), 1-34. doi:10.1145/1409220.1409225Lipczak, M., & Milios, E. (2010). The impact of resource title on tags in collaborative tagging systems. Proceedings of the 21st ACM conference on Hypertext and hypermedia - HT ’10. doi:10.1145/1810617.1810648Marinho, L. B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., & Symeonidis, P. (2010). Social Tagging Recommender Systems. Recommender Systems Handbook, 615-644. doi:10.1007/978-0-387-85820-3_19Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. Proceedings of the seventeenth conference on Hypertext and hypermedia - HYPERTEXT ’06. doi:10.1145/1149941.1149949Mathes , A. 2004 Folksonomies-Cooperative classification and communication through shared metadata http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.htmlMelenhorst, M., & van Setten, M. (2007). Usefulness of Tags in Providing Access to Large Information Systems. 2007 IEEE International Professional Communication Conference. doi:10.1109/ipcc.2007.4464070Millen, D., Feinberg, J., & Kerr, B. (2005). Social bookmarking in the enterprise. Queue, 3(9), 28. doi:10.1145/1105664.1105676Robu, V., Halpin, H., & Shepherd, H. (2009). Emergence of consensus and shared vocabularies in collaborative tagging systems. ACM Transactions on the Web, 3(4), 1-34. doi:10.1145/1594173.1594176Schmitz, C., Hotho, A., Jäschke, R., & Stumme, G. (s. f.). Mining Association Rules in Folksonomies. Data Science and Classification, 261-270. doi:10.1007/3-540-34416-0_28Smith , G. 2004 Atomiq: Folksonomy: social classification http://atomiq.org/archives/2004/08/folksonomy_social_classification.htmlSubramanya, S. B., & Liu, H. (2008). Socialtagger - collaborative tagging for blogs in the long tail. Proceeding of the 2008 ACM workshop on Search in social media - SSM ’08. doi:10.1145/1458583.1458588Au Yeung, C., Gibbins, N., & Shadbolt, N. (2009). Contextualising tags in collaborative tagging systems. Proceedings of the 20th ACM conference on Hypertext and hypermedia - HT ’09. doi:10.1145/1557914.1557958Zhang, N., Zhang, Y., & Tang, J. (2009). A tag recommendation system for folksonomy. Proceeding of the 2nd ACM workshop on Social web search and mining - SWSM ’09. doi:10.1145/1651437.165144

Learning to recommend descriptive tags for questions in social forums

10.1145/2559157ACM Transactions on Information Systems321-ATIS

Relación entre el crowdsourcing y la inteligencia colectiva: el caso de los sistemas de etiquetado social

El crowdsourcing es un término acuñado recientemente que hace referencia a un tipo de iniciativas que se dan en Internet. En estas iniciativas, alguien, ya sea una empresa, una persona o una institucion, propone a la multitud de Internet la realización de una tarea a cambio de una recompensa. Para que estas iniciativas se puedan llevar a cabo, Internet, y más concretamente, el desarrollo de la Web 2.0, ha sido fundamental. Internet, además de suponer la base tecnológica sobre la que se asienta el crowdsourcing, permite a este tipo de iniciativas tener acceso a cientos de miles de individuos de cualquier parte del mundo. Al haber sido un término acuñado recientemente, la literatura existente es escasa, realidad que va subsanándose paulatinamente. Además, las fronteras conceptuales del término son difusas. Por esta razón, muchas veces se confunde el crowdsourcing con procesos relacionados aunque no exactamente iguales, como la innovación abierta, la co-creación o la inteligencia colectiva. La presente tesis tiene como objetivo clarificar cual es exactamente la relación existente entre el crowdsourcing y uno de estos fenómenos: la inteligencia colectiva. Con este fin, se analizarán los sistemas de etiquetado social, una aplicación Web 2.0 claramente perteneciente al ámbito de la Inteligencia Colectiva, para observar las diferencias y semejanzas entre ésta y el crowdsourcing. En el camino que se recorre para identificar y analizar esta relación, se alcanzan otros hitos relevantes que ayudan a conseguir el objetivo de la tesis. En lo que al crowdsourcing respecta, se ha definido este término en base a ocho elementos, lo que facilita la identificación de qué es o no crowdsourcing. También se ha desarrollado una tipología de iniciativas de crowdsourcing en base a otras tipologías propuestas por diferentes autores. En cuanto a los sistemas de etiquetado social, se ha analizado y descrito el uso que hacen los usuarios de las etiquetas que describen los recursos de Internet, además de explicar como estos sistemas pueden favorecer los procesos de investigación colaborativos.Estellés Arolas, E. (2013). Relación entre el crowdsourcing y la inteligencia colectiva: el caso de los sistemas de etiquetado social [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31661TESI

SocialTagger- Collaborative Tagging for Blogs in the Long Tail

Social bookmarking is the process through which users share tags for online resources like blogs with others. Such collaborative tags provide valuable metadata for retrieval systems. While the successes of collaborative tagging systems have been demonstrated by popular websites like Del.icio.us, these sites cover only a small fraction of the available blogs on the web. The vast majority of the blogs are not available on any collaborative tagging system and are often tagged only by the authors. This lack of coverage of collaborative tags is a considerable roadblock in using the tag metadata in a web scale information retrieval system. To solve this problem we propose and implement a system to automatically recommend collaborative tags for a blog. The automatically generated tags will help to surface the blogs by making them available on social book marking sites and allow them to be easily discovered and potentially further tagged by a wider population

Predictive Modeling for Navigating Social Media

Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs of users, two issues are fundamental, namely link sparseness and object selection. Link sparseness is observed for many resources that are untagged or inadequately tagged, hindering navigation to the resources. Object selection is concerned when there are a large number of information objects that are linked to the current object, requiring to select the more interesting or relevant ones for guiding navigation effectively. This dissertation focuses on three dimensions, namely the semantic, social and temporal dimensions, to address link sparseness and object selection. To address link sparseness, we study the task of tag prediction. This task aims to enrich tags for the untagged or inadequately tagged resources, such that the predicted tags can serve as navigable links to these resources. For this task, we take a topic modeling approach to exploit the latent semantic relationships between resource content and tags. To address object selection, we study the task of personalized tag recommendation and trend discovery using social annotations. Personalized tag recommendation leverages the collective wisdom from the social tagging community to recommend tags that are semantically relevant to the target resource, while being tailored to the tagging preferences of individual users. For this task, we propose a probabilistic framework which leverages the implicit social links between like-minded users, i.e. who show similar tagging preferences, to recommend suitable tags. Social tags capture the interest of the users in the annotated resources at different times. These social annotations allow us to construct temporal profiles for the annotated resources. By analyzing these temporal profiles, we unveil the non-trivial temporal trends of the annotated resources, which provide novel metrics for selecting relevant and interesting resources for guiding navigation. For trend discovery using social annotations, we propose a trend discovery process which enables us to analyze trends for a multitude of semantics encapsulated in the temporal profiles of the annotated resources