Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Incorporating query expansion and quality indicators in searching microblog posts
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Learning recommender systems with adaptive regularization
Proceedings of the fifth ACM international conference on Web search and data mining
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Exploiting real-time information retrieval in the microblogosphere
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Learning to rank method has been proposed for practical application in the field of information retrieval. When employing it in microblog retrieval, the significant interactions of various involved features are rarely considered. In this paper, we propose a Ranking Factorization Machine (Ranking FM) model, which applies Factorization Machine model to microblog ranking on basis of pairwise classification. In this way, our proposed model combines the generality of learning to rank framework with the advantages of factorization models in estimating interactions between features, leading to better retrieval performance. Moreover, three groups of features (content relevance features, semantic expansion features and quality features) and their interactions are utilized in the Ranking FM model with the methods of stochastic gradient descent and adaptive regularization for optimization. Experimental results demonstrate its superiority over several baseline systems on a real Twitter dataset in terms of P@30 and MAP metrics. Furthermore, it outperforms the best performing results in the TREC'12 Real-Time Search Task.