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Exploiting Syntactic and Neighbourhood Attributes to Address Cold Start in Tag Recommendation by Fabiano M. Belém; André G. Heringer; Jussara M. Almeida; Marcos A. Gonçalves is a Computer Science article available to read on EtoBox.

What is Exploiting Syntactic and Neighbourhood Attributes to Address Cold Start in Tag Recommendation about?

Many state-of-the-art tag recommendation methods were designed considering that an initial set of tags is available in the target object. However, the effectiveness of these methods greatly suffer in a cold start scenario in which those initial tags are absent (although other features of the target object, such as title and description, may be present). To tackle this problem, previous work extracts candidate terms directly from the text associated with the target object or from similar/related objects, and use statistical properties of the occurrence of words, such as term frequency (TF) and inverse document frequency (IDF), to rank the candidate tags for recommendation. Yet, these properties, in isolation, may not be enough to effectively rank candidate tags, specially when they are extracted from the typically small and possibly low quality texts associated with Web 2.0 objects. In this work, we analyze various syntactic patterns (e.g., syntactic dependencies between words in a sentence) of the text associated with Web 2.0 objects that can be exploited to identify and recommend tags. We also propose new tag quality attributes based on these patterns, including them as new eviden

Who reads Exploiting Syntactic and Neighbourhood Attributes to Address Cold Start in Tag Recommendation?

Author

Fabiano M. Belém; André G. Heringer; Jussara M. Almeida; Marcos A. Gonçalves

Publisher

Elsevier BV

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)