Semantic tag recommendation using concept model
|Semantic tag recommendation using concept model|
|Author(s)||Li C., Datta A., Sun A.|
|Published in||SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Keyword(s)||Concept model, Semantic tag, Tag recommendation, Wikipedia (Extra: Concept model, Concept space, Data sets, Multiple user, Recommendation accuracy, Speed-ups, Tag recommendation, Wikipedia, Behavioral research, Semantics, Information retrieval)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Semantic tag recommendation using concept model is a 2011 conference paper written in English by Li C., Datta A., Sun A. and published in SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.
The common tags given by multiple users to a particular document are often semantically relevant to the document and each tag represents a specific topic. In this paper, we attempt to emulate human tagging behavior to recommend tags by considering the concepts contained in documents. Specifically, we represent each document using a few most relevant concepts contained in the document, where the concept space is derived from Wikipedia. Tags are then recommended based on the tag concept model derived from the annotated documents of each tag. Evaluated on a Delicious dataset of more than 53K documents, the proposed technique achieved comparable tag recommendation accuracy as the state-of-the-art, while yielding an order of magnitude speed-up.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.