A Comparison of Content-Based Tag Recommendations in Folksonomy Systems

Illig, Jens; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd

doi:10.1007/978-3-642-22140-8_9

Cited by 23 publications

(19 citation statements)

References 17 publications

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“…To this aim, item signals need to be translated into tag signals. Illig et al [2011] report an evaluation of different algorithms of content-based tag recommendation in a cold-start scenario on a large real-world dataset-a crawl of the delicious bookmarking system. The cold-start problem occurs when a bookmark is uploaded for the first time and therefore no information provided by other users can be used.…”

Section: Related Workmentioning

confidence: 99%

Social semantic query expansion

Biancalana

Gasparetti

Micarelli

et al. 2013

ACM Trans. Intell. Syst. Technol.

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Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

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Section: Related Workmentioning

confidence: 99%

Social semantic query expansion

Biancalana

Gasparetti

Micarelli

et al. 2013

ACM Trans. Intell. Syst. Technol.

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“…Recommendation algorithms and multiclass classifiers are very useful in allocating tags to a bookmark in social bookmarking systems . A multinomial naive Bayes categorization mechanism is employed for categorizing the tag.…”

Section: Review Of Recommendation Algorithmsmentioning

confidence: 99%

“…Recommendation algorithms and multiclass classifiers are very useful in allocating tags to a bookmark in social bookmarking systems. 11 A multinomial naive Bayes categorization mechanism is employed for categorizing the tag. The new feature from the training set, which is unknown by the set, has been treated evenly to the wildcard feature during categorization.…”

Section: Multinomial Naive Bayes Categorizationmentioning

confidence: 99%

A Review of Ontology-Based Tag Recommendation Approaches

Subramaniyaswamy

Vijayakumar

Indragandhi

2013

Int. J. Intell. Syst.

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Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.

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“…Similarly a tag-document bipartite graph has been used as the basis to cluster tags and documents, as discussed in [21]. In addition to these e↵orts, there have been many other approaches for tag recommendation [15,9,4,28,30].…”

Section: Related Workmentioning

confidence: 99%

Predicting semantic annotations on the real-time web

Khabiri

Caverlee

Kamath

2012

Proceedings of the 23rd ACM Conference on Hypertext and Social Media

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The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via user-driven linking, in which users annotate their own status updates with lightweight semantic annotations -or hashtags. Unfortunately, there is evidence that hashtag growth is not keeping pace with the growth of the overall real-time web. In a random sample of 3 million tweets, we find that only 10.2% contain at least one hashtag. Hence, in this paper we explore the possibility of predicting hashtags for un-annotated status updates. Toward this end, we propose and evaluate a graph-based prediction framework. Three of the unique features of the approach are: (i) a path aggregation technique for scoring the closeness of terms and hashtags in the graph; (ii) pivot term selection, for identifying high value terms in status updates; and (iii) a dynamic sliding window for recommending hashtags reflecting the current status of the real-time web. Experimentally we find encouraging results in comparison with Bayesian and data mining-based approaches.

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A Comparison of Content-Based Tag Recommendations in Folksonomy Systems

Cited by 23 publications

References 17 publications

Social semantic query expansion

Social semantic query expansion

A Review of Ontology-Based Tag Recommendation Approaches

Predicting semantic annotations on the real-time web

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