Information Retrieval and Folksonomies together for Recommender Systems (original) (raw)

On social semantic relations for recommending tags and resources using folksonomies

2012

Social tagging is an innovative and powerful mechanism introduced by social Web: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. However, due to the lack of rules for managing the tagging process and of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications do not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing; for this reason the most recent research in this area is dedicated to empower the social perspective applying semantic approaches in order to support tagging, browsing, searching, and adaptive personaliza-tion in innovative recommender systems. This paper proposes a survey on existing recommender systems, discussing how they extract social semantic relation (i.e relations among users, resources and tags of a folksonomy), and how they utilize this knowledge for recommending resources and tags.

Categorising social tags to improve folksonomy-based recommendations

2011

In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks.

Tag Recommendations in Folksonomies

Lecture Notes in Computer Science, 2007

Collaborative tagging systems allow users to assign keywords-so called "tags"-to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we present two tag recommendation algorithms: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, an adaptation of the well-known PageRank algorithm that can cope with undirected triadic hyperedges. We evaluate and compare both algorithms on large-scale real life datasets and show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably. * A shortened version of this work has been published at PKDD 2007. 1

A personalized recommender system based on users' information in folksonomies

Proceedings of the 22nd International Conference on World Wide Web, 2013

Thanks to the high popularity and simplicity of folksonomies, many users tend to share objects (movies, songs, bookmarks, etc.) by annotating them with a set of tags of their own choice. Users represent the core of the system since they are both the contributors and the creators of the information. Yet, each user has its own profile and its own ideas making thereby the strength as well as the weakness of folksonomies. Indeed, it would be helpful to take account of users' profile when suggesting a list of tags and resources or even a list of friends, in order to make a more personal recommandation. The goal is to suggest tags (or resources) which may correspond to a user's vocabulary or interests rather than a list of most used and popular tags in folksonomies. In this paper, we consider users' profile as a new dimension of a folksonomy classically composed of three dimensions <users, tags, ressources> and we propose an approach to group users with equivalent profiles and equivalent interests as quadratic concepts. Then, we use quadratic concepts in order to propose our personalized recommendation system of users, tags and resources according to each user's profile. Carried out experiments on the large-scale real-world filmography dataset MovieLens highlight encouraging results in terms of precision.

A tag recommender system exploiting user and community behavior

… Systems & the …, 2009

The Social Web has been enjoying huge popularity in recent years, attracting millions of visitors on sites such as Facebook, Delicious or YouTube. Today, we are no longer mere consumers of information, but we also actively participate in social networks, upload our personal photos, share our bookmarks, write web logs and annotate and comment on the information provided by others. Following the exponential growth in the popularity of Social Web sites, many traditional, non-social sites, are now implementing social features. Likewise many enterprises are deploying internal social media sites to support expertise location and sharing of work-related information and knowledge. The Social Web therefore provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.  New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders can not only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.  Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. This social layer can also be used as evidence on which to infer relationships and trust levels between users for recommendation generation.  The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people. Or, the design and development of more interactive and richer recommender system user interfaces that enable users to express their opinions and preferences in an intuitive and effortless manner.

On the Role of Social Tags in Filtering Interesting Resources from Folksonomies

Social tagging systems allow users to easily create, organize and share collections of resources (e.g. Web pages, research papers, photos, etc.) in a collaborative fashion. The rise in popularity of these systems in recent years go along with an rapid increase in the amount of data contained in their underlying folksonomies, thereby hindering the user task of discovering interesting resources. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class classification is evaluated as a means to learn how to identify relevant information based on positive examples exclusively, since it is assumed that users expressed their interest in resources by annotating them while there is not an straightforward method to collect non-interesting information. The results of using social tags for personal classification are compared with those achieved with traditional information sources about the user interests such as the textual content of Web documents. Finding interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities. Experimental evaluation showed that tag-based classification outperformed classifiers learned using the full-text of documents as well as other content-related sources.

Social Tagging Recommender Systems

Recommender Systems Handbook, 2010

The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the "noise" that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS. We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions. 615 1 The term folksonomy refers to a blend of the two words folk and taxonomy, i.e., a collaborative classification system created and maintained by ordinary users. 2

Exploring folksonomy for personalized search

Research and Development in Information Retrieval, 2008

As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.