Tag recommendations in social bookmarking systems

Jäschke, Robert; Marinho, Leandro Balby; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd

doi:10.3233/aic-2008-0438

Cited by 132 publications

(59 citation statements)

References 26 publications

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“…We conducted our prediction study on the same datasets and training/test set splits as in our empirical study (see Section 2.2). This allowed us to train the algorithms based on the posts in the training set and compare the top-10 tags that an algorithm predicted for user u and resource r of a post in the test set with the set of relevant tags in this test set post [8,9]. Based on that, we computed two prediction accuracy metrics known from research on information retrieval and recommender systems.…”

Section: Methodsmentioning

confidence: 99%

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

Kowald

Lex

2016

Proceedings of the 27th ACM Conference on Hypertext and Social Media

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In this paper, we study factors that influence tag reuse behavior in social tagging systems. Our work is guided by the activation equation of the cognitive model ACT-R, which states that the usefulness of information in human memory depends on the three factors usage frequency, recency and semantic context. It is our aim to shed light on the influence of these factors on tag reuse. In our experiments, we utilize six datasets from the social tagging systems Flickr, CiteULike, BibSonomy, Delicious, LastFM and MovieLens, covering a range of various tagging settings. Our results confirm that frequency, recency and semantic context positively influence the reuse probability of tags. However, the extent to which each factor individually influences tag reuse strongly depends on the type of folksonomy present in a social tagging system. Our work can serve as guideline for researchers and developers of tag-based recommender systems when designing algorithms for social tagging environments.

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Section: Methodsmentioning

confidence: 99%

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

Kowald

Lex

2016

Proceedings of the 27th ACM Conference on Hypertext and Social Media

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“…To alleviate potential bias caused by using only one data set containing social tags, we investigated whether we could include a data set from Bibsonomy , another leading bibliographic management system, in our study. Unfortunately, the Bibsonomy data set (Jäschke, Marinho, Hotho, Schmidt‐Thieme, & Stumme, ) contained too few biomedical articles to enable a meaningful comparison. Beyond Bibsonomy, no other data set provided social tags on a large enough scale for the purposes of our study.…”

Section: Data Set and Processingmentioning

confidence: 99%

Social tagging is no substitute for controlled indexing: A comparison of Medical Subject Headings and CiteULike tags assigned to 231,388 papers

Lee

Schleyer

2012

J Am Soc Inf Sci Tec

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Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings (MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing.

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“…In order to compare the performance of our proposed approach, we compared our approach with a popular tagging approach [38], [39] based on classical cosine similarity and presented as (cosine_CF), which depends on users' tagging histories. The results of this comparison are discussed in the next section.…”

Section: ) Dataset and Evaluation Matricesmentioning

confidence: 99%

Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

Ghabayen¹,

Noah²

2017

International Journal on Advanced Science, Engineering and Information Technology

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Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., "liked-minded" users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to "cold-start" users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users' interests because there is no intersection between users' transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper, we present a new collaborative filtering approach based on users' semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.

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Tag recommendations in social bookmarking systems

Cited by 132 publications

References 26 publications

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

Social tagging is no substitute for controlled indexing: A comparison of Medical Subject Headings and CiteULike tags assigned to 231,388 papers

Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

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