Personalized and object-centered tag recommendation methods for Web 2.0 applications

作者：

Highlights：

• We propose new heuristics for object-centered and personalized tag recommendation.

• We also propose new learning-to-rank (L2R) based strategies for the same tasks.

• They exploit tag co-occurrences, textual features, relevance metrics and user history.

• Our solutions greatly outperform state-of-the-art methods on real datasets.

• Tag personalization produces better descriptions of the objects.

摘要

•We propose new heuristics for object-centered and personalized tag recommendation.•We also propose new learning-to-rank (L2R) based strategies for the same tasks.•They exploit tag co-occurrences, textual features, relevance metrics and user history.•Our solutions greatly outperform state-of-the-art methods on real datasets.•Tag personalization produces better descriptions of the objects.

论文关键词：Tag recommendation,Relevance metrics,Personalization

论文评审过程：Received 19 April 2013, Revised 5 March 2014, Accepted 17 March 2014, Available online 10 April 2014.

论文官网地址：https://doi.org/10.1016/j.ipm.2014.03.002