Social recommender systems for web 2.0 folksonomies

Abstract

The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

“…and social tagging systems (STS) provides powerful way for users to organize, administer, consolidate and search for innumerable kinds of resources. These tags [8], [9] , [12] , [15] , [17], [18] carry interesting information about the preference of users who make the tags and of course about the labelled items itself. For example, Last.fm allows users showcase their preferences by tagging artists, albums or music tracks and Del.icio.us allows users to tag webpages.…”

“…Users annotate an item such as photos, videos, blogs etc., for which is otherwise difficult to generate attributes, by introducing a tag. A set of triples -user, item, tag form information spaces referred to as folksonomies [12] .Recommending tags can serve various purposes, such as: increasing the chances of getting an entity annotated, reminding a user what an entity is about and consolidating the annotation across the users [15]. The collection of all his assignments is called his personomy, the collection of all personomies constitutes the folksonomy.…”

“…The study showed that the common relationship model between users, items, and annotations is often not sufficient for constructing accurate recommendation algorithms in folksonomies. Personalized models which consider user's personal data and the local neighborhood for modeling provide higher accuracy at the noticeably lower computational and modeling costs [12]. Nan Zheng, Qiudan Li (2011) investigated the usefulness of tag and time information in predicting user's preference and integrated this information into CF for building effective resource-recommendation model in Social Tagging Systems (STS).…”

“…The growing size of folksonomies poses interesting challenges and opportunities for searching and mining useful content and finding other users sharing the same interests [12]. Analysis of such "Big" data is the one of the key issues faced by designers of recommender systems.…”

“…This led to the second phase of the evolution in recommendation system. With the rapid expansion of Web 2.0, these systems incorporated social information [8] [9], [10], [11], [12] along with information used in traditional recommendation system, leading to the development of Social based Recommendation systems [13], [14], [15], [2] [16], [17] [18]. This social information was related to the virtual social circle of the user.…”