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
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
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and topicality in interactive information retrieval
Journal of the American Society for Information Science and Technology
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Multiple approaches to analysing query diversity
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Diversifying web search results
Proceedings of the 19th international conference on World wide web
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient diversification of web search results
Proceedings of the VLDB Endowment
Identifying and ranking possible semantic and common usage categories of search engine queries
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Intent-aware search result diversification
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Evaluating diversified search results using per-intent graded relevance
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Intent-based diversification of web search results: metrics and algorithms
Information Retrieval
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Current search engines do not explicitly take different meanings and usages of user queries into consideration when they rank the search results. As a result, they tend to retrieve results that cover the most popular meanings or usages of the query. Consequently, users who want results that cover a rare meaning or usage of query or results that cover all different meanings/usages may have to go through a large number of results in order to find the desired ones. Another problem with current search engines is that they do not adequately take users' intention into consideration. In this paper, we introduce a novel result ranking algorithm (mNIR) that explicitly takes result novelty, user intention-based distribution and result relevancy into consideration and mixes them to achieve better result ranking. We analyze how giving different emphasis to the above three aspects would impact the overall ranking of the results. Our approach builds on our previous method for identifying and ranking possible categories of any user query based on the meanings and usages of the terms and phrases within the query. These categories are also used to generate category queries for retrieving results matching different meanings/usages of the original user query. Our experimental results show that the proposed algorithm can outperform state-of-the-art diversification approaches.