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
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
The Journal of Machine Learning Research
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A platform for Okapi-based contextual information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A reranking model for genomics aspect search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia
IEEE Transactions on Knowledge and Data Engineering
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In information retrieval, we are interested in the information that is not only relevant but also novel. In this paper, we study how to boost novelty for biomedical information retrieval through probabilistic latent semantic analysis. We conduct the study based on TREC Genomics Track data. In TREC Genomics Track, each topic is considered to have an arbitrary number of aspects, and the novelty of a piece of information retrieved, called a passage, is assessed based on the amount of new aspects it contains. In particular, the aspect performance of a ranked list is rewarded by the number of new aspects reached at each rank and penalized by the amount of irrelevant passages that are rated higher than the novel ones. Therefore, to improve aspect performance, we should reach as many aspects as possible and as early as possible. In this paper, we make a preliminary study on how probabilistic latent semantic analysis can help capture different aspects of a ranked list, and improve its performance by re-ranking. Experiments indicate that the proposed approach can greatly improve the aspect-level performance over baseline algorithm Okapi BM25.