Hybrid tag recommendation for social annotation systems

Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad; Burke, Robin

doi:10.1145/1871437.1871543

Cited by 32 publications

(18 citation statements)

References 13 publications

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“…Beyond STRec, there are a lot of investigations on graphbased tag recommenders which rely on links between users (e.g., social relation, tagging behavior),items and/or tags [13], [15], [8], [21]. One of the first is FolkRank [13].…”

Section: Related Workmentioning

confidence: 99%

“…In [21] and [8], the authors combine some simple recommenders (e.g., popular tags used to annotate an item, the ones by a user and those of his neighborhood). In the experimentation in [8], Gemmell et al showed that their proposition gives results among the best.…”

Section: Related Workmentioning

confidence: 99%

“…In the experimentation in [8], Gemmell et al showed that their proposition gives results among the best. However they extracted some postcores from the datasets they used in order to focus on the dense core of the folksonomy graph.…”

Section: Related Workmentioning

confidence: 99%

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A Social and Popularity-Based Tag Recommender

Abdessalem

Nacke

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Tag recommendation aims to recommend to a user the most suited tags for a given item. It is an important functionality of resource sharing systems. In this paper we propose a recommendation algorithm, called FasTag, that links the relevance of the tags to both their popularity and the opinions of the user's neighbors. FasTag assumes that the users are organized in a weighted graph representing, for instance, the similarity or the trust between them.The two salient aspects of FasTag are the scoring function it uses to evaluate the relevance of the tags, and its low computation cost. Thus, FasTag can make online recommendations, even for large datasets.Moreover, we improve the accuracy of its recommendations by adjusting automatically the size of the recommended list of tags. The experiments we did on several datasets show a significant improvement of the accuracy of the recommendations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Social and Popularity-Based Tag Recommender

Abdessalem

Nacke

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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“…Additionally, the proliferation of social tagging has created new topics for the research community. One of the emerging topics is a tag recommender system, supporting the suggestion of suitable tags during tagging processes [6]. To induce tags' full potential for facilitating better search results or item recommendations, tag recommender systems help users describe/organize their content with less effort.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in terms of personalization, the tag recommenders should deal with three-dimensional data, i.e., users, tags, and items, generally leading to require more time-consuming tasks. Therefore, a number of studies have explored various techniques for personalized tag recommendations, such as collaborative filtering [6], link structure analysis [7], association rule mining [9], popularity models [11], tensor decompositions [15], probabilistic models [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

Personalized PageRank vectors for tag recommendations

Kim

Saddik

2011

Proceedings of the Fifth ACM Conference on Recommender Systems

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This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.

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A survey on tag recommendation methods

Belém

Almeida

Gonçalves

2016

Asso for Info Science & Tech

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Tags (keywords freely assigned by users to describe web content) have become highly popular on Web 2.0 applications, because of the strong stimuli and easiness for users to create and describe their own content. This increase in tag popularity has led to a vast literature on tag recommendation methods. These methods aim at assisting users in the tagging process, possibly increasing the quality of the generated tags and, consequently, improving the quality of the information retrieval (IR) services that rely on tags as data sources. Regardless of the numerous and diversified previous studies on tag recommendation, to our knowledge, no previous work has summarized and organized them into a single survey article. In this article, we propose a taxonomy for tag recommendation methods, classifying them according to the target of the recommendations, their objectives, exploited data sources, and underlying techniques. Moreover, we provide a critical overview of these methods, pointing out their advantages and disadvantages. Finally, we describe the main open challenges related to the field, such as tag ambiguity, cold start, and evaluation issues.

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Hybrid tag recommendation for social annotation systems

Cited by 32 publications

References 13 publications

A Social and Popularity-Based Tag Recommender

A Social and Popularity-Based Tag Recommender

Personalized PageRank vectors for tag recommendations

A survey on tag recommendation methods

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