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.
Although the latter mentioned approaches perform reasonable well, they are computational expensive compared to simple "most popular tags" approaches. Furthermore, they ignore recent observations made in social tagging systems, such as the variation of the individual tagging behavior over time @cite_21 . To that end, recent research has made first promising steps towards more accurate graph-based models that also account for the variable of time @cite_11 @cite_24 . The approaches have shown to outperform some of the current state-of-the-art tag recommender algorithms.
@cite_24 @cite_21 @cite_11
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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. 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. In social bookmarking systems, existing methods in tag prediction have shown that the performance of prediction can be significantly improved by modeling users' preferences. However, these preferences are usually treated as constant over time, neglecting the temporal factor within users' behaviors. In this paper, we study the problem of session-like behavior in social tagging systems and demonstrate that the predictive performance can be improved by considering sessions. Experiments, conducted on three public datasets, show that our session-based method can outperform baselines and two state-of-the-art algorithms significantly.