IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
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Most existing learning to rank methods only use content relevance of objects with respect to queries to rank objects. However, they ignore relationships among objects. In this paper, two types of relationships between objects, topic based similarity and word based similarity, are combined together to improve the performance of a ranking model. The two types of similarities are calculated using LDA andtf-idf methods, respectively. A novel ranking function is constructed based on the similarity information. Traditional gradient descent algorithm is used to train the ranking function. Experimental results prove that the proposed ranking function has better performance than the traditional ranking function and the ranking function only incorporating word based similarity between documents.