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.
Related to the latter strand of research, we present in this paper a novel graph-based tag recommender mechanism that uses the BLL equation which is based on the principles of a popular model of human cognition called ACT-R (e.g., @cite_10 ). We show that the approach is not only extremely simple but also reveal that the algorithm outperforms current state-of-the-art graph-based (e.g., @cite_2 @cite_4 @cite_15 ) and the leading time-based @cite_24 tag recommender approaches.
@cite_4 @cite_24 @cite_2 @cite_15 @cite_10
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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 services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios. 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. Adaptive control of thought–rational (ACT–R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT–R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.