Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The budgeted maximum coverage problem
Information Processing Letters
Modern Information Retrieval
Machine Learning
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the 18th international conference on World wide web
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
A comparative analysis of cascade measures for novelty and diversity
Proceedings of the fourth ACM international conference on Web search and data mining
Improving tag-based recommendation by topic diversification
ECIR'11 Proceedings of the 33rd European conference on Advances 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
Efficient Tag Recommendation for Real-Life Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
On the role of novelty for search result diversification
Information Retrieval
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
Exploiting novelty and diversity in tag recommendation
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Tag recommendation approaches have historically focused on maximizing the relevance of the recommended tags for a given object, such as a movie or a song. Nevertheless, different users may be interested in the same object for different reasons---for instance, the Star Wars movies may appeal to both adventure as well as to fantasy movie fans. In this situation, a sensible strategy is to provide a user with diverse recommendations of how to tag the object. In this paper, we address the problem of recommending relevant and diverse tags as a ranking problem. In particular, we propose a novel tag recommendation approach that explicitly takes into account the possible topics (e.g., categories) underlying an object in order to promote tags with high coverage and low redundancy with respect to these topics. We thoroughly evaluate our proposed approach using data collected from two popular Web 2.0 applications, namely, LastFM and MovieLens. Our experimental results attest the effectiveness of our approach at promoting more relevant and diverse tags in contrast to state-of-the-art relevance-based methods as well as a recently proposed method that takes both relevance and diversity into account.