In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how adding a time-dependent component outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We show how effective principles for information retrieval can be designed and implemented if human memory processes are taken into account.
Recent years have shown that tagging is an important feature of the Social Web supporting the users with a simple mechanism to collaboratively organize and finding content @cite_12 . Although tagging has been shown to significantly improve search @cite_30 (and in particular tags provided by the individual), it is also known that users are typically lazy in providing tags for instance for their bookmarked resources. It is therefore not surprising that recent research has taken up this challenge to support the individual in her tag application process in the form of personalized tag recommenders. To date, the two following approaches have been established -- graph based and content-based tag recommender systems @cite_32 . In our work we focus on graph-based approaches.
@cite_30 @cite_32 @cite_12
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Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout. 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 evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably. Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation systems generally model user behavior as research has shown that accuracy can be significantly improved by modeling users' preferences. However, these preferences are usually treated as constant over time, neglecting the temporal factor within users' interests. On the other hand, little is known about how this factor may influence prediction in social bookmarking systems. In this paper, we investigate the temporal dynamics of user interests in tagging systems and propose a user-tag-specific temporal interests model for tracking users' interests over time. Additionally, we analyze the phenomenon of topic switches in social bookmarking systems, showing that a temporal interests model can benefit from the integration of topic switch detection and that temporal characteristics of social tagging systems are different from traditional concept drift problems. We conduct experiments on three public datasets, demonstrating the importance of personalization and user-tag specialization in tagging systems. Experimental results show that our method can outperform state-of-the-art tag prediction algorithms. We also incorporate our model within existing content-based methods yielding significant improvements in performance. The emergence of social tagging systems enables users to organize and share their interested resources. In order to ease the human-computer interaction with such systems, extensive researches have been done on how to recommend personalized tags for rescources. These researches mainly consider user profile, resource content, or the graph structure of users, resources and tags. Users' preferences towards different tags are usually regarded as invariable against time, neglecting the switch of users' short-term interests. In this paper, we examine the temporal factor in users' tagging behaviors by investigating the occurrence patterns of tags and then incorporate this into a novel method for ranking tags. To assess a tag for a user-resource pair, we first consider the user's general interest in it, then we calculate its recurrence probability based on the temporal usage pattern, and at last we consider its tag relevance to the content of the post. Experiments conducted on real datasets from Bibsonomy and Delicious demonstrate that our method outperforms other temporal models and state-of-the-art tag prediction methods.