Exploiting novelty and diversity in tag recommendation

Authors:
Fabiano Belém;Eder Martins;Jussara Almeida;Marcos Gonçalves
Affiliations:
Computer Science Department, Federal University of Minas Gerais, Brazil;Computer Science Department, Federal University of Minas Gerais, Brazil;Computer Science Department, Federal University of Minas Gerais, Brazil;Computer Science Department, Federal University of Minas Gerais, Brazil
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

Citing 11
Cited 1

Modern Information Retrieval

Modern Information Retrieval
MultiTube--Where Web 2.0 and Multimedia Could Meet

IEEE MultiMedia
A combined component approach for finding collection-adapted ranking functions based on genetic programming

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Flickr tag recommendation based on collective knowledge

Proceedings of the 17th international conference on World Wide Web
Personalized, interactive tag recommendation for flickr

Proceedings of the 2008 ACM conference on Recommender systems
Learning to tag

Proceedings of the 18th international conference on World wide web
Social media recommendation based on people and tags

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporal diversity in recommender systems

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Associative tag recommendation exploiting multiple textual features

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rank and relevance in novelty and diversity metrics for recommender systems

Proceedings of the fifth ACM conference on Recommender systems
Assessing the quality of textual features in social media

Information Processing and Management: an International Journal

Topic diversity in tag recommendation

Proceedings of the 7th ACM conference on Recommender systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The design and evaluation of tag recommendation methods have focused only on relevance. However, other aspects such as novelty and diversity may be as important to evaluate the usefulness of the recommendations. In this work, we define these two aspects in the context of tag recommendation and propose a novel recommendation strategy that considers them jointly with relevance. This strategy extends a state-of-the-art method based on Genetic Programming to include novelty and diversity metrics both as attributes and as part of the objective function. We evaluate the proposed strategy using data collected from 3 popular Web 2.0 applications: LastFM, YouTube and YahooVideo. Our experiments show that our strategy outperforms the state-of-the-art alternative in terms of novelty and diversity, without harming relevance.