Web search behaviour studies, including eye-tracking studies of search result examination, have resulted in numerous insights to improve search result quality and presentation. Yet, eye tracking studies have been restricted in scale, due to the expense and the effort required. Furthermore, as the reach of the Web expands, it becomes increasingly important to understand how searchers around the world see and interact with the search results. To address both challenges, we introduce ViewSer, a novel methodology for performing web search examination studies remotely, at scale, and without requiring eye-tracking equipment. ViewSer operates by automatically modifying the appearance of a search engine result page, to clearly show one search result at a time as if through a "viewport", while partially blurring the rest and allowing the participant to move the viewport naturally with a computer mouse or trackpad. Remarkably, the resulting result viewing and clickthrough patterns agree closely with unrestricted viewing of results, as measured by eye-tracking equipment, validated by a study with over 100 participants. We also explore applications of ViewSer to practical search tasks, such as analyzing the search result summary (snip- pet) attractiveness, result re-ranking, and evaluating snippet quality. These experiments could have only be done previously by tracking the eye movements for a small number of subjects in the lab. In contrast, our study was performed with over 100 participants, allowing us to reproduce and extend previous findings, establishing ViewSer as a valuable tool for large-scale search behavior experiments.
{"title":"ViewSer: enabling large-scale remote user studies of web search examination and interaction","authors":"Dmitry Lagun, Eugene Agichtein","doi":"10.1145/2009916.2009967","DOIUrl":"https://doi.org/10.1145/2009916.2009967","url":null,"abstract":"Web search behaviour studies, including eye-tracking studies of search result examination, have resulted in numerous insights to improve search result quality and presentation. Yet, eye tracking studies have been restricted in scale, due to the expense and the effort required. Furthermore, as the reach of the Web expands, it becomes increasingly important to understand how searchers around the world see and interact with the search results. To address both challenges, we introduce ViewSer, a novel methodology for performing web search examination studies remotely, at scale, and without requiring eye-tracking equipment. ViewSer operates by automatically modifying the appearance of a search engine result page, to clearly show one search result at a time as if through a \"viewport\", while partially blurring the rest and allowing the participant to move the viewport naturally with a computer mouse or trackpad. Remarkably, the resulting result viewing and clickthrough patterns agree closely with unrestricted viewing of results, as measured by eye-tracking equipment, validated by a study with over 100 participants. We also explore applications of ViewSer to practical search tasks, such as analyzing the search result summary (snip- pet) attractiveness, result re-ranking, and evaluating snippet quality. These experiments could have only be done previously by tracking the eye movements for a small number of subjects in the lab. In contrast, our study was performed with over 100 participants, allowing us to reproduce and extend previous findings, establishing ViewSer as a valuable tool for large-scale search behavior experiments.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127512211","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}
L. Shou, Ke Chen, Gang Chen, Chao Zhang, Yi Ma, X. Zhang
The cyber-physical systems (CPS) are envisioned as a class of real-time systems integrating the computing, communication and storage facilities with monitoring and control of the physical world. One interesting CPS application in the mobile Internet is to provide Web search "on the spot" regarding the physical world that a user sees, or literally WYRIWYS (What-You-Retrieve-Is-What-You-See). The objective of our work is to develop server/browser software for supporting WYRIWYS search in our prototype cyber-physical search engine. A WYRIWYS search retrieves visible Web objects and ranks them by their cyber-physical relevances (term, visual, spatial, temporal etc.). This work is distinguished from previous LWS as it provides quality Web search geared with the physical world. Therefore it suggests a very promising solution to cyber-physical Web search.
{"title":"What-you-retrieve-is-what-you-see: a preliminary cyber-physical search engine","authors":"L. Shou, Ke Chen, Gang Chen, Chao Zhang, Yi Ma, X. Zhang","doi":"10.1145/2009916.2010156","DOIUrl":"https://doi.org/10.1145/2009916.2010156","url":null,"abstract":"The cyber-physical systems (CPS) are envisioned as a class of real-time systems integrating the computing, communication and storage facilities with monitoring and control of the physical world. One interesting CPS application in the mobile Internet is to provide Web search \"on the spot\" regarding the physical world that a user sees, or literally WYRIWYS (What-You-Retrieve-Is-What-You-See). The objective of our work is to develop server/browser software for supporting WYRIWYS search in our prototype cyber-physical search engine. A WYRIWYS search retrieves visible Web objects and ranks them by their cyber-physical relevances (term, visual, spatial, temporal etc.). This work is distinguished from previous LWS as it provides quality Web search geared with the physical world. Therefore it suggests a very promising solution to cyber-physical Web search.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123254757","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 consider the problem of estimating the relative expertise score of users in community question and answering services (CQA). Previous approaches typically only utilize the explicit question answering relationship between askers and an-swerers and apply link analysis to address this problem. The im-plicit pairwise comparison between two users that is implied in the best answer selection is ignored. Given a question and answering thread, it's likely that the expertise score of the best answerer is higher than the asker's and all other non-best answerers'. The goal of this paper is to explore such pairwise comparisons inferred from best answer selections to estimate the relative expertise scores of users. Formally, we treat each pairwise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8 CQA task data with 3 million questions and introducing answer quality prediction based evaluation metrics, the experimental results show that the pairwise comparison based competition model significantly outperforms link analysis based approaches (PageRank and HITS) and pointwise approaches (number of best answers and best answer ratio) for estimating the expertise of active users. Furthermore, it's shown that pairwise comparison based competi-tion models have better discriminative power than other methods. It's also found that answer quality (best answer) is an important factor to estimate user expertise.
{"title":"Competition-based user expertise score estimation","authors":"Jing Liu, Young-In Song, Chin-Yew Lin","doi":"10.1145/2009916.2009975","DOIUrl":"https://doi.org/10.1145/2009916.2009975","url":null,"abstract":"In this paper, we consider the problem of estimating the relative expertise score of users in community question and answering services (CQA). Previous approaches typically only utilize the explicit question answering relationship between askers and an-swerers and apply link analysis to address this problem. The im-plicit pairwise comparison between two users that is implied in the best answer selection is ignored. Given a question and answering thread, it's likely that the expertise score of the best answerer is higher than the asker's and all other non-best answerers'. The goal of this paper is to explore such pairwise comparisons inferred from best answer selections to estimate the relative expertise scores of users. Formally, we treat each pairwise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8 CQA task data with 3 million questions and introducing answer quality prediction based evaluation metrics, the experimental results show that the pairwise comparison based competition model significantly outperforms link analysis based approaches (PageRank and HITS) and pointwise approaches (number of best answers and best answer ratio) for estimating the expertise of active users. Furthermore, it's shown that pairwise comparison based competi-tion models have better discriminative power than other methods. It's also found that answer quality (best answer) is an important factor to estimate user expertise.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532952","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 consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial document ranking, user interaction with the system produces relevance feedback (RF) which the system then uses to revise the ranking. This interactive process repeats until the user terminates the search. To maximize accuracy relative to user effort, we propose an active learning strategy. At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it. Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user. Evaluation on Robust04 under noisy feedback and on LETOR collections further demonstrate the effectiveness of active learning, as well as value of negative feedback in this task scenario.
{"title":"Active learning to maximize accuracy vs. effort in interactive information retrieval","authors":"Aibo Tian, Matthew Lease","doi":"10.1145/2009916.2009939","DOIUrl":"https://doi.org/10.1145/2009916.2009939","url":null,"abstract":"We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial document ranking, user interaction with the system produces relevance feedback (RF) which the system then uses to revise the ranking. This interactive process repeats until the user terminates the search. To maximize accuracy relative to user effort, we propose an active learning strategy. At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it. Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user. Evaluation on Robust04 under noisy feedback and on LETOR collections further demonstrate the effectiveness of active learning, as well as value of negative feedback in this task scenario.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116847582","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}
Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.
{"title":"Intent-oriented diversity in recommender systems","authors":"S. Vargas, P. Castells, D. Vallet","doi":"10.1145/2009916.2010124","DOIUrl":"https://doi.org/10.1145/2009916.2010124","url":null,"abstract":"Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128968612","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}
Yoshiyuki Inagaki, Jiang Bian, Yi Chang, Motoko Maki
Mobile search is still in infancy compared with general purpose web search. With limited training data and weak relevance features, the ranking performance in mobile search is far from satisfactory. To address this problem, we propose to leverage the knowledge of Web search to enhance the ranking of mobile search. In this paper, we first develop an equivalent page conversion between web search and mobile search, then we design a few novel ranking features, generated from the click-through data in web search, for estimating the relevance of mobile search. Large scale evaluations demonstrate that the knowledge from web search is quite effective for boosting the relevance of ranking on mobile search.
{"title":"Enhancing mobile search using web search log data","authors":"Yoshiyuki Inagaki, Jiang Bian, Yi Chang, Motoko Maki","doi":"10.1145/2009916.2010119","DOIUrl":"https://doi.org/10.1145/2009916.2010119","url":null,"abstract":"Mobile search is still in infancy compared with general purpose web search. With limited training data and weak relevance features, the ranking performance in mobile search is far from satisfactory. To address this problem, we propose to leverage the knowledge of Web search to enhance the ranking of mobile search. In this paper, we first develop an equivalent page conversion between web search and mobile search, then we design a few novel ranking features, generated from the click-through data in web search, for estimating the relevance of mobile search. Large scale evaluations demonstrate that the knowledge from web search is quite effective for boosting the relevance of ranking on mobile search.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124677859","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}
Predicting searcher success and satisfaction is a key problem in Web search, which is essential for automatic evaluating and improving search engine performance. This problem has been studied actively in the desktop search setting, but not specifically for mobile search, despite many known differences between the two modalities. As mobile devices become increasingly popular for searching the Web, improving the searcher experience on such devices is becoming crucially important. In this paper, we explore the possibility of predicting searcher success and satisfaction in mobile search with a smart phone. Specifically, we investigate client-side interaction signals, including the number of browsed pages, and touch screen-specific actions such as zooming and sliding. Exploiting this information with machine learning techniques results in nearly 80% accuracy for predicting searcher success -- significantly outperforming the previous models.
{"title":"Detecting success in mobile search from interaction","authors":"Qi Guo, Shuai Yuan, Eugene Agichtein","doi":"10.1145/2009916.2010133","DOIUrl":"https://doi.org/10.1145/2009916.2010133","url":null,"abstract":"Predicting searcher success and satisfaction is a key problem in Web search, which is essential for automatic evaluating and improving search engine performance. This problem has been studied actively in the desktop search setting, but not specifically for mobile search, despite many known differences between the two modalities. As mobile devices become increasingly popular for searching the Web, improving the searcher experience on such devices is becoming crucially important. In this paper, we explore the possibility of predicting searcher success and satisfaction in mobile search with a smart phone. Specifically, we investigate client-side interaction signals, including the number of browsed pages, and touch screen-specific actions such as zooming and sliding. Exploiting this information with machine learning techniques results in nearly 80% accuracy for predicting searcher success -- significantly outperforming the previous models.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126674080","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}
Yen-Ta Huang, A. Cheng, Liang-Chi Hsieh, Winston H. Hsu, Kuo-Wei Chang
We propose a novel model for landmark discovery that locates region-based landmarks on map in contrast to the traditional point-based landmarks. The proposed method preserves more information and automatically identifies candidate regions on map by crowdsourcing geo-referenced photos. Gaussian kernel convolution is applied to remove noises and generate detected region. We adopt F1 measure to evaluate discovered landmarks and manually check the association between tags and regions. The experiment results show that more than 90% of attractions in the selected city can be correctly located by this method.
{"title":"Region-based landmark discovery by crowdsourcing geo-referenced photos","authors":"Yen-Ta Huang, A. Cheng, Liang-Chi Hsieh, Winston H. Hsu, Kuo-Wei Chang","doi":"10.1145/2009916.2010089","DOIUrl":"https://doi.org/10.1145/2009916.2010089","url":null,"abstract":"We propose a novel model for landmark discovery that locates region-based landmarks on map in contrast to the traditional point-based landmarks. The proposed method preserves more information and automatically identifies candidate regions on map by crowdsourcing geo-referenced photos. Gaussian kernel convolution is applied to remove noises and generate detected region. We adopt F1 measure to evaluate discovered landmarks and manually check the association between tags and regions. The experiment results show that more than 90% of attractions in the selected city can be correctly located by this method.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116363294","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 majority of the current information retrieval models weight the query concepts (e.g., terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features. Unlike the existing supervised ranking methods, our model learns importance weights not only for the explicit query concepts, but also for the latent concepts that are associated with the query through pseudo-relevance feedback. The experimental results on both newswire and web TREC corpora show that our model consistently and significantly outperforms a wide range of state-of-the-art retrieval models. In addition, our model significantly reduces the number of latent concepts used for query expansion compared to the non-parameterized pseudo-relevance feedback based models.
{"title":"Parameterized concept weighting in verbose queries","authors":"Michael Bendersky, Donald Metzler, W. Bruce Croft","doi":"10.1145/2009916.2009998","DOIUrl":"https://doi.org/10.1145/2009916.2009998","url":null,"abstract":"The majority of the current information retrieval models weight the query concepts (e.g., terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features. Unlike the existing supervised ranking methods, our model learns importance weights not only for the explicit query concepts, but also for the latent concepts that are associated with the query through pseudo-relevance feedback. The experimental results on both newswire and web TREC corpora show that our model consistently and significantly outperforms a wide range of state-of-the-art retrieval models. In addition, our model significantly reduces the number of latent concepts used for query expansion compared to the non-parameterized pseudo-relevance feedback based models.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121551360","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}
When searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval model in order to improve relevance ranking. Previous work has followed one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, or 2) a probabilistic model generating a query from the textual and temporal part of a document independently. In this paper, we compare the effectiveness of different time-aware ranking methods by using a mixture model applied to all methods. Extensive evaluation is conducted using the New York Times Annotated Corpus, queries and relevance judgments obtained using the Amazon Mechanical Turk.
{"title":"A comparison of time-aware ranking methods","authors":"Nattiya Kanhabua, K. Nørvåg","doi":"10.1145/2009916.2010147","DOIUrl":"https://doi.org/10.1145/2009916.2010147","url":null,"abstract":"When searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval model in order to improve relevance ranking. Previous work has followed one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, or 2) a probabilistic model generating a query from the textual and temporal part of a document independently. In this paper, we compare the effectiveness of different time-aware ranking methods by using a mixture model applied to all methods. Extensive evaluation is conducted using the New York Times Annotated Corpus, queries and relevance judgments obtained using the Amazon Mechanical Turk.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876380","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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