Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
Self-indexing inverted files for fast text retrieval
ACM Transactions on Information Systems (TOIS)
Optimization of inverted vector searches
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Vector-space ranking with effective early termination
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Impact transformation: effective and efficient web retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Combining fuzzy information: an overview
ACM SIGMOD Record
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Optimization strategies for complex queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Simplified similarity scoring using term ranks
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient and self-tuning incremental query expansion for top-k query processing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pruned query evaluation using pre-computed impacts
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient query expansion with auxiliary data structures
Information Systems
Efficient document retrieval in main memory
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient processing of complex features for information retrieval
Efficient processing of complex features for information retrieval
Efficient query expansion for advertisement search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Positional relevance model for pseudo-relevance feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A unified optimization framework for robust pseudo-relevance feedback algorithms
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient term proximity search with term-pair indexes
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Fast query expansion using approximations of relevance models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A boosting approach to improving pseudo-relevance feedback
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Faster top-k document retrieval using block-max indexes
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Efficiency optimizations for interpolating subqueries
Proceedings of the 20th ACM international conference on Information and knowledge management
Proximity-based rocchio's model for pseudo relevance
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Pseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a two-round retrieval process: the first round is to retrieve an initial set of documents relevant to an original query, and the second round is to retrieve final retrieval results using the original query expanded with terms selected from the previously retrieved documents. This two-round retrieval process is clearly time consuming, which could arguably be one of main reasons that hinder the wide adaptation of the pseudo-relevance feedback methods in real-world IR systems. In this paper, we study how to improve the efficiency of pseudo-relevance feedback methods. The basic idea is to reduce the time needed for the second round of retrieval by leveraging the query processing results of the first round. Specifically, instead of processing the expand query as a newly submitted query, we propose an incremental approach, which resumes the query processing results (i.e. document accumulators) for the first round of retrieval and process the second round of retrieval mainly as a step of adjusting the scores in the accumulators. Experimental results on TREC Terabyte collections show that the proposed incremental approach can improve the efficiency of pseudo-relevance feedback methods by a factor of two without sacrificing their effectiveness.