Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic -- the 'Topic Sentiment'. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the 'minority' or outlying sentiment(s) are boosted and those with the popular sentiment are demoted. Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48% is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.
{"title":"Sentiment diversification with different biases","authors":"Elif Aktolga, James Allan","doi":"10.1145/2484028.2484060","DOIUrl":"https://doi.org/10.1145/2484028.2484060","url":null,"abstract":"Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic -- the 'Topic Sentiment'. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the 'minority' or outlying sentiment(s) are boosted and those with the popular sentiment are demoted. Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48% is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132220263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a general information access evaluation framework that can potentially handle summaries, ranked document lists and even multi query sessions seamlessly. Our framework first builds a trailtext which represents a concatenation of all the texts read by the user during a search session, and then computes an evaluation metric called U-measure over the trailtext. Instead of discounting the value of a retrieved piece of information based on ranks, U-measure discounts it based on its position within the trailtext. U-measure takes the document length into account just like Time-Biased Gain (TBG), and has the diminishing return property. It is therefore more realistic than rank-based metrics. Furthermore, it is arguably more flexible than TBG, as it is free from the linear traversal assumption (i.e., that the user scans the ranked list from top to bottom), and can handle information access tasks other than ad hoc retrieval. This paper demonstrates the validity and versatility of the U-measure framework. Our main conclusions are: (a) For ad hoc retrieval, U-measure is at least as reliable as TBG in terms of rank correlations with traditional metrics and discriminative power; (b) For diversified search, our diversity versions of U-measure are highly correlated with state-of-the-art diversity metrics; (c) For multi-query sessions, U-measure is highly correlated with Session nDCG; and (d) Unlike rank-based metrics such as DCG, U-measure can quantify the differences between linear and nonlinear traversals in sessions. We argue that our new framework is useful for understanding the user's search behaviour and for comparison across different information access styles (e.g. examining a direct answer vs. examining a ranked list of web pages).
{"title":"Summaries, ranked retrieval and sessions: a unified framework for information access evaluation","authors":"T. Sakai, Zhicheng Dou","doi":"10.1145/2484028.2484031","DOIUrl":"https://doi.org/10.1145/2484028.2484031","url":null,"abstract":"We introduce a general information access evaluation framework that can potentially handle summaries, ranked document lists and even multi query sessions seamlessly. Our framework first builds a trailtext which represents a concatenation of all the texts read by the user during a search session, and then computes an evaluation metric called U-measure over the trailtext. Instead of discounting the value of a retrieved piece of information based on ranks, U-measure discounts it based on its position within the trailtext. U-measure takes the document length into account just like Time-Biased Gain (TBG), and has the diminishing return property. It is therefore more realistic than rank-based metrics. Furthermore, it is arguably more flexible than TBG, as it is free from the linear traversal assumption (i.e., that the user scans the ranked list from top to bottom), and can handle information access tasks other than ad hoc retrieval. This paper demonstrates the validity and versatility of the U-measure framework. Our main conclusions are: (a) For ad hoc retrieval, U-measure is at least as reliable as TBG in terms of rank correlations with traditional metrics and discriminative power; (b) For diversified search, our diversity versions of U-measure are highly correlated with state-of-the-art diversity metrics; (c) For multi-query sessions, U-measure is highly correlated with Session nDCG; and (d) Unlike rank-based metrics such as DCG, U-measure can quantify the differences between linear and nonlinear traversals in sessions. We argue that our new framework is useful for understanding the user's search behaviour and for comparison across different information access styles (e.g. examining a direct answer vs. examining a ranked list of web pages).","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134442877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we propose a novel framework for modeling the uniqueness of the user preferences for recommendation systems. User uniqueness is determined by learning to what extent the user's item preferences deviate from those of an "average user" in the system. Based on this framework, we suggest three different recommendation strategies that trade between uniqueness and conformity. Using two real item datasets, we demonstrate the effectiveness of our uniqueness based recommendation framework.
{"title":"Modeling the uniqueness of the user preferences for recommendation systems","authors":"Haggai Roitman, David Carmel, Y. Mass, I. Eiron","doi":"10.1145/2484028.2484102","DOIUrl":"https://doi.org/10.1145/2484028.2484102","url":null,"abstract":"In this paper we propose a novel framework for modeling the uniqueness of the user preferences for recommendation systems. User uniqueness is determined by learning to what extent the user's item preferences deviate from those of an \"average user\" in the system. Based on this framework, we suggest three different recommendation strategies that trade between uniqueness and conformity. Using two real item datasets, we demonstrate the effectiveness of our uniqueness based recommendation framework.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.
{"title":"Time-aware structured query suggestion","authors":"Taiki Miyanishi, T. Sakai","doi":"10.1145/2484028.2484143","DOIUrl":"https://doi.org/10.1145/2484028.2484143","url":null,"abstract":"Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134073784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistical translation models and latent semantic analysis (LSA) are two effective approaches to exploiting click-through data for Web search ranking. While the former learns semantic relationships between query terms and document terms directly, the latter maps a document and the queries for which it has been clicked to vectors in a lower dimensional semantic space. This paper presents two document ranking models that combine the strengths of both the approaches by explicitly modeling word-pairs. The first model, called PairModel, is a monolingual ranking model based on word-pairs derived from click-through data. It maps queries and documents into a concept space spanned by these word-pairs. The second model, called Bilingual Paired Topic Model (BPTM), uses bilingual word translations and can jointly model query-document collections written in multiple languages. This model uses topics to capture term dependencies and maps queries and documents in multiple languages into a lower dimensional semantic sub-space spanned by the topics. These models are evaluated on the Web search task using real world data sets in three different languages. Results show that they consistently outperform various state-of-the-art baseline models, and the best result is obtained by interpolating PairModel and BPTM.
{"title":"Modeling click-through based word-pairs for web search","authors":"Jagadeesh Jagarlamudi, Jianfeng Gao","doi":"10.1145/2484028.2484082","DOIUrl":"https://doi.org/10.1145/2484028.2484082","url":null,"abstract":"Statistical translation models and latent semantic analysis (LSA) are two effective approaches to exploiting click-through data for Web search ranking. While the former learns semantic relationships between query terms and document terms directly, the latter maps a document and the queries for which it has been clicked to vectors in a lower dimensional semantic space. This paper presents two document ranking models that combine the strengths of both the approaches by explicitly modeling word-pairs. The first model, called PairModel, is a monolingual ranking model based on word-pairs derived from click-through data. It maps queries and documents into a concept space spanned by these word-pairs. The second model, called Bilingual Paired Topic Model (BPTM), uses bilingual word translations and can jointly model query-document collections written in multiple languages. This model uses topics to capture term dependencies and maps queries and documents in multiple languages into a lower dimensional semantic sub-space spanned by the topics. These models are evaluated on the Web search task using real world data sets in three different languages. Results show that they consistently outperform various state-of-the-art baseline models, and the best result is obtained by interpolating PairModel and BPTM.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133793223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Live Nugget Extractor system provides users with a method of efficiently and accurately collecting relevant information for any web query rather than providing a simple ranked lists of documents. The system utilizes an online learning procedure to infer relevance of unjudged documents while extracting and ranking information from judged documents. This creates a set of judged and inferred relevance scores for both documents and text fragments, which can be used for test collections, summarization, and other tasks where high accuracy and large collections with minimal human effort are needed.
Live Nugget Extractor系统为用户提供了一种有效而准确地收集任何网络查询相关信息的方法，而不是提供简单的文档排名列表。该系统利用在线学习程序来推断未被判断的文档的相关性，同时从被判断的文档中提取信息并对其进行排序。这将为文档和文本片段创建一组判断和推断的相关性分数，这些分数可用于测试收集、摘要和其他任务，在这些任务中，需要最少的人力来进行高精度和大型收集。
{"title":"Live nuggets extractor: a semi-automated system for text extraction and test collection creation","authors":"Matthew Ekstrand-Abueg, Virgil Pavlu, J. Aslam","doi":"10.1145/2484028.2484211","DOIUrl":"https://doi.org/10.1145/2484028.2484211","url":null,"abstract":"The Live Nugget Extractor system provides users with a method of efficiently and accurately collecting relevant information for any web query rather than providing a simple ranked lists of documents. The system utilizes an online learning procedure to infer relevance of unjudged documents while extracting and ranking information from judged documents. This creates a set of judged and inferred relevance scores for both documents and text fragments, which can be used for test collections, summarization, and other tasks where high accuracy and large collections with minimal human effort are needed.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a method for finding impressive creators in online social network sites (SNSs). Many users are actively engaged in publishing their own works, sharing visual content on sites such as YouTube or Flickr. In this paper, we focus on the Japanese illustration-sharing SNS, Pixiv. We implement an illustrator search system based on user impression categories. The impressions of illustrators are estimated from clues in the crowdsourced social-tag annotations on their illustrations. We evaluated our system in terms of normalized discounted cumulative gain and found that using feedback on motifs and impressions for illustrations of relevant illustrators improved illustrator search by 11%.
{"title":"Finding impressive social content creators: searching for SNS illustrators using feedback on motifs and impressions","authors":"Yohei Seki, Kiyoto Miyajima","doi":"10.1145/2484028.2484133","DOIUrl":"https://doi.org/10.1145/2484028.2484133","url":null,"abstract":"We propose a method for finding impressive creators in online social network sites (SNSs). Many users are actively engaged in publishing their own works, sharing visual content on sites such as YouTube or Flickr. In this paper, we focus on the Japanese illustration-sharing SNS, Pixiv. We implement an illustrator search system based on user impression categories. The impressions of illustrators are estimated from clues in the crowdsourced social-tag annotations on their illustrations. We evaluated our system in terms of normalized discounted cumulative gain and found that using feedback on motifs and impressions for illustrations of relevant illustrators improved illustrator search by 11%.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134322014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Internet advertising, a form of advertising that utilizes the Internet to deliver marketing messages and attract customers, has seen exponential growth since its inception around twenty years ago; it has been pivotal to the success of the World Wide Web. The dramatic growth of internet advertising poses great challenges to information retrieval, machine learning, data mining and game theory, and it calls for novel technologies to be developed. The main purpose of this workshop is to bring together researchers and practitioners in the area of Internet Advertising and enable them to share their latest research results, to express their opinions, and to discuss future directions.
{"title":"Internet advertising: theory and practice","authors":"Bin Gao, Jun Yan, Dou Shen, Tie-Yan Liu","doi":"10.1145/2484028.2484221","DOIUrl":"https://doi.org/10.1145/2484028.2484221","url":null,"abstract":"Internet advertising, a form of advertising that utilizes the Internet to deliver marketing messages and attract customers, has seen exponential growth since its inception around twenty years ago; it has been pivotal to the success of the World Wide Web. The dramatic growth of internet advertising poses great challenges to information retrieval, machine learning, data mining and game theory, and it calls for novel technologies to be developed. The main purpose of this workshop is to bring together researchers and practitioners in the area of Internet Advertising and enable them to share their latest research results, to express their opinions, and to discuss future directions.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125093456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. D. Buccio, Giorgio Maria Di Nunzio, G. Silvello
Digital Geolinguistic systems encourage collaboration between linguists, historians, archaeologists, ethnographers, as they explore the relationship between language and cultural adaptation and change. In this demo, we propose a Linked Open Data approach for increasing the level of interoperability of geolinguistic applications and the reuse of the data. We present a case study of a geolinguistic project named Atlante Sintattico d'Italia, Syntactic Atlas of Italy (ASIt).
{"title":"A geolinguistic web application based on linked open data","authors":"E. D. Buccio, Giorgio Maria Di Nunzio, G. Silvello","doi":"10.1145/2484028.2484219","DOIUrl":"https://doi.org/10.1145/2484028.2484219","url":null,"abstract":"Digital Geolinguistic systems encourage collaboration between linguists, historians, archaeologists, ethnographers, as they explore the relationship between language and cultural adaptation and change. In this demo, we propose a Linked Open Data approach for increasing the level of interoperability of geolinguistic applications and the reuse of the data. We present a case study of a geolinguistic project named Atlante Sintattico d'Italia, Syntactic Atlas of Italy (ASIt).","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The need for a search engine to deal with ambiguous queries has been known for a long time (diversification). However, it is only recently that this need has become a focus within information retrieval research. How to respond to indications that a result is relevant to a query (relevance feedback) has also been a long focus of research. When thinking about the results for a query as being clustered by topic, these two areas of information retrieval research appear to be opposed to each other. Interestingly though, they both appear to improve the performance of search engines, raising the question: they can be combined or made to work with each other? When presented with an ambiguous query there are a number of techniques that can be employed to better select results. The primary technique being researched now is diversification, which aims to populate the results with a set of documents that cover different possible interpretations for the query, while maintaining a degree of relevance, as determined by the search engine. For example, given a query of "java" it is unclear whether the user, without any other information, means the programming language, the coffee, the island of Indonesia or a multitude of other meanings. In order to do this the assumption that documents are independent of each other when assessing potential relevance has to be broken. That is, a documents relevance, as calculated by the search engine, is no longer dependent only on the query, but also the other documents that have been selected. How a document is identified as being similar to previously selected documents, and the trade off between estimated relevance and topic coverage are current areas for information retrieval research. For unambiguous queries, or for search engines that do not perform diversification, it is possible to improve the results selected by reacting to information identifying a given result as truly relevant or not. This mechanism is known as relevance feedback. The most common response to relevance feedback is to investigate the documents for their most content-bearing terms, and either add, or subtract, their influence to a newly formed query which is then re-run on the remaining documents to re-order them. There has been a scant amount of research into the combination of these methods. However, Carbonell et al. [1] show that an initially diverse result set can provide a better approach for identifying the topic a user is interested in for a relevance feedback style approach. This approach was further extended by Raman et al. [4]. An important aspect of relevance feedback is the selection of documents to use. In the 2008 TREC relevance feedback track, Meij et al. [3] generated a diversified result set which outperformed other rankings as a source of feedback documents. The use of pseudo-relevance feedback (assuming the top ranked documents are relevant) to extract sub-topics for use in diversification was explored by Santos et al. [5]. These prev
{"title":"Diversified relevance feedback","authors":"Matt Crane","doi":"10.1145/2484028.2484227","DOIUrl":"https://doi.org/10.1145/2484028.2484227","url":null,"abstract":"The need for a search engine to deal with ambiguous queries has been known for a long time (diversification). However, it is only recently that this need has become a focus within information retrieval research. How to respond to indications that a result is relevant to a query (relevance feedback) has also been a long focus of research. When thinking about the results for a query as being clustered by topic, these two areas of information retrieval research appear to be opposed to each other. Interestingly though, they both appear to improve the performance of search engines, raising the question: they can be combined or made to work with each other? When presented with an ambiguous query there are a number of techniques that can be employed to better select results. The primary technique being researched now is diversification, which aims to populate the results with a set of documents that cover different possible interpretations for the query, while maintaining a degree of relevance, as determined by the search engine. For example, given a query of \"java\" it is unclear whether the user, without any other information, means the programming language, the coffee, the island of Indonesia or a multitude of other meanings. In order to do this the assumption that documents are independent of each other when assessing potential relevance has to be broken. That is, a documents relevance, as calculated by the search engine, is no longer dependent only on the query, but also the other documents that have been selected. How a document is identified as being similar to previously selected documents, and the trade off between estimated relevance and topic coverage are current areas for information retrieval research. For unambiguous queries, or for search engines that do not perform diversification, it is possible to improve the results selected by reacting to information identifying a given result as truly relevant or not. This mechanism is known as relevance feedback. The most common response to relevance feedback is to investigate the documents for their most content-bearing terms, and either add, or subtract, their influence to a newly formed query which is then re-run on the remaining documents to re-order them. There has been a scant amount of research into the combination of these methods. However, Carbonell et al. [1] show that an initially diverse result set can provide a better approach for identifying the topic a user is interested in for a relevance feedback style approach. This approach was further extended by Raman et al. [4]. An important aspect of relevance feedback is the selection of documents to use. In the 2008 TREC relevance feedback track, Meij et al. [3] generated a diversified result set which outperformed other rankings as a source of feedback documents. The use of pseudo-relevance feedback (assuming the top ranked documents are relevant) to extract sub-topics for use in diversification was explored by Santos et al. [5]. These prev","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129711131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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