Proactive contextual information systems help people locate information by automatically suggesting potentially relevant resources based on their current tasks or interests. Such systems are becoming increasingly popular, but designing user interfaces that effectively communicate recommended information is a challenge: the interface must be unobtrusive, yet communicate enough information at the right time to provide value to the user. In this paper we describe our experience with the FXPAL Bar, a proactive information system designed to provide contextual access to corporate and personal resources. In particular, we present three features designed to communicate proactive recommendations more effectively: translucent recommendation windows increase the user's awareness of particularly highly-ranked recommendations, query term highlighting communicates the relationship between a recommended document and the user's current context, and a novel recommendation digest function allows users to return to the most relevant previously recommended resources. We present empirical evidence supporting our design decisions and relate lessons learned for other designers of contextual recommendation systems.
{"title":"Improving proactive information systems","authors":"Daniel Billsus, D. Hilbert, Dan Maynes-Aminzade","doi":"10.1145/1040830.1040869","DOIUrl":"https://doi.org/10.1145/1040830.1040869","url":null,"abstract":"Proactive contextual information systems help people locate information by automatically suggesting potentially relevant resources based on their current tasks or interests. Such systems are becoming increasingly popular, but designing user interfaces that effectively communicate recommended information is a challenge: the interface must be unobtrusive, yet communicate enough information at the right time to provide value to the user. In this paper we describe our experience with the FXPAL Bar, a proactive information system designed to provide contextual access to corporate and personal resources. In particular, we present three features designed to communicate proactive recommendations more effectively: translucent recommendation windows increase the user's awareness of particularly highly-ranked recommendations, query term highlighting communicates the relationship between a recommended document and the user's current context, and a novel recommendation digest function allows users to return to the most relevant previously recommended resources. We present empirical evidence supporting our design decisions and relate lessons learned for other designers of contextual recommendation systems.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133655974","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}
Voicemail has become an integral part of our personal and professional communication. The number of messages that accumulate in our voice mailboxes necessitate new ways of prioritizing them. Currently, we are forced to actively listen to all messages in order to find out which ones are important and which ones can be attended to later on. In this paper, we describe Emotive Alert, a system that can detect some of the significant emotions in a new message and notify the account owner along various affective axes, including urgency, formality, valence (happy vs. sad) and arousal (calm vs. excited). We have used a purely acoustic, HMM-based approach for identifying the emotions, which allows application of this system to all messages independent of language.
{"title":"Emotive alert: HMM-based emotion detection in voicemail messages","authors":"Zeynep Inanoglu, R. Caneel","doi":"10.1145/1040830.1040885","DOIUrl":"https://doi.org/10.1145/1040830.1040885","url":null,"abstract":"Voicemail has become an integral part of our personal and professional communication. The number of messages that accumulate in our voice mailboxes necessitate new ways of prioritizing them. Currently, we are forced to actively listen to all messages in order to find out which ones are important and which ones can be attended to later on. In this paper, we describe Emotive Alert, a system that can detect some of the significant emotions in a new message and notify the account owner along various affective axes, including urgency, formality, valence (happy vs. sad) and arousal (calm vs. excited). We have used a purely acoustic, HMM-based approach for identifying the emotions, which allows application of this system to all messages independent of language.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124755858","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}
Charles B. Callaway, T. Kuflik, E. Not, A. Novello, O. Stock, M. Zancanaro
Museum visitors can continue interacting with museum exhibits even after they have left the museum. We can help them do this by creating a report that includes a basic, personalized narration of their visit, the items and relationships they found most interesting, pointers to additional related online information, and suggestions for future visits to the current and other museums. In this work we describe the automatic generation of personalized natural language reports to help create one episode in an ongoing coherent sequence of cultural activities.
{"title":"Personal reporting of a museum visit as an entrypoint to future cultural experience","authors":"Charles B. Callaway, T. Kuflik, E. Not, A. Novello, O. Stock, M. Zancanaro","doi":"10.1145/1040830.1040896","DOIUrl":"https://doi.org/10.1145/1040830.1040896","url":null,"abstract":"Museum visitors can continue interacting with museum exhibits even after they have left the museum. We can help them do this by creating a report that includes a basic, personalized narration of their visit, the items and relationships they found most interesting, pointers to additional related online information, and suggestions for future visits to the current and other museums. In this work we describe the automatic generation of personalized natural language reports to help create one episode in an ongoing coherent sequence of cultural activities.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130209443","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}
Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.
{"title":"Trust in recommender systems","authors":"J. O'Donovan, B. Smyth","doi":"10.1145/1040830.1040870","DOIUrl":"https://doi.org/10.1145/1040830.1040870","url":null,"abstract":"Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766384","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}
Researchers have noticed that readers are increasingly skimming instead of reading in depth. Skimming also occur in re-reading activities, where the goal is to recall specific topical facts. Bookmarks and highlighters were invented precisely to achieve this goal. For skimming activities, readers need effective ways to direct their attention toward the most relevant passages within text. We describe how we have enhanced skimming activity by conceptually highlighting sentences within electronic text that relate to search keywords. We perform the conceptual highlighting by computing what conceptual keywords are related to each other via word co-occurrence and spreading activation. Spreading activation is a cognitive model developed in psychology to simulate how memory chunks and conceptual items are retrieved in our brain. We describe the method used, and illustrate the idea with realistic scenarios using our system.
{"title":"ScentHighlights: highlighting conceptually-related sentences during reading","authors":"Ed H. Chi, Lichan Hong, M. Gumbrecht, S. Card","doi":"10.1145/1040830.1040895","DOIUrl":"https://doi.org/10.1145/1040830.1040895","url":null,"abstract":"Researchers have noticed that readers are increasingly skimming instead of reading in depth. Skimming also occur in re-reading activities, where the goal is to recall specific topical facts. Bookmarks and highlighters were invented precisely to achieve this goal. For skimming activities, readers need effective ways to direct their attention toward the most relevant passages within text. We describe how we have enhanced skimming activity by conceptually highlighting sentences within electronic text that relate to search keywords. We perform the conceptual highlighting by computing what conceptual keywords are related to each other via word co-occurrence and spreading activation. Spreading activation is a cognitive model developed in psychology to simulate how memory chunks and conceptual items are retrieved in our brain. We describe the method used, and illustrate the idea with realistic scenarios using our system.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122160800","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 describe a tool for transcribing voice generated percussive rhythms. The system consists of: (a) a segmentation component which separates the monophonic input stream into percussive events (b) a descriptors generation component that computes a set of acoustic features from each of the extracted segments, (c) a machine learning component which assigns to each of the segmented sounds of the input stream a symbolic class. We describe each of these components and compare different machine learning strategies that can be used to obtain a symbolic representation of the oral percussive performance.
{"title":"Towards automatic transcription of expressive oral percussive performances","authors":"Amaury Hazan","doi":"10.1145/1040830.1040904","DOIUrl":"https://doi.org/10.1145/1040830.1040904","url":null,"abstract":"We describe a tool for transcribing voice generated percussive rhythms. The system consists of: (a) a segmentation component which separates the monophonic input stream into percussive events (b) a descriptors generation component that computes a set of acoustic features from each of the extracted segments, (c) a machine learning component which assigns to each of the segmented sounds of the input stream a symbolic class. We describe each of these components and compare different machine learning strategies that can be used to obtain a symbolic representation of the oral percussive performance.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127786298","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}
C. Kray, A. Butz, A. Krüger, A. Schmidt, H. Prendinger
This second workshop on Multi-User and Ubiquitous User Interfaces aims at further investigating two major issues identified at last year's MU3I: control and consistency. The former relates to how a user gains control of devices in a ubiquitous computing environment, how control is passed, and how it is shared in such a setting. The second one concerns interfaces that span multiple devices or move from one set of devices to another. Both issues will be discussed in this year's workshop (with a focus on consistency.
{"title":"Multi-user and ubiquitous user interfaces: (MU3I 2005)","authors":"C. Kray, A. Butz, A. Krüger, A. Schmidt, H. Prendinger","doi":"10.1145/1040830.1040837","DOIUrl":"https://doi.org/10.1145/1040830.1040837","url":null,"abstract":"This second workshop on Multi-User and Ubiquitous User Interfaces aims at further investigating two major issues identified at last year's MU3I: control and consistency. The former relates to how a user gains control of devices in a ubiquitous computing environment, how control is passed, and how it is shared in such a setting. The second one concerns interfaces that span multiple devices or move from one set of devices to another. Both issues will be discussed in this year's workshop (with a focus on consistency.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314927","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}
I will talk about an emerging class of user interfaces that if not exactly intelligent are at least attention-reactive. They are being developed to handle "sensemaking" tasks, in which users find, analyze, and creation products or action from large collections of documents. Applications might be expected to develop in law, education, scholarship, security, and medicine. These interfaces have a focus + context visualization on the front end and a semantic contextual computing engine on the back end. Ultimately they can be expected to have mixed initiatives. These interfaces require the development of a supporting science of human information interaction that stresses interaction between the user and information and deemphasizes the platform through which this occurs.
{"title":"Attention-reactive user interface for sensemaking","authors":"S. Card","doi":"10.1145/1040830.1040831","DOIUrl":"https://doi.org/10.1145/1040830.1040831","url":null,"abstract":"I will talk about an emerging class of user interfaces that if not exactly intelligent are at least attention-reactive. They are being developed to handle \"sensemaking\" tasks, in which users find, analyze, and creation products or action from large collections of documents. Applications might be expected to develop in law, education, scholarship, security, and medicine. These interfaces have a focus + context visualization on the front end and a semantic contextual computing engine on the back end. Ultimately they can be expected to have mixed initiatives. These interfaces require the development of a supporting science of human information interaction that stresses interaction between the user and information and deemphasizes the platform through which this occurs.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131589262","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}
XML has emerged as the primary standard of data representation and data exchange [13]. Although many software tools exist to assist the XML implementation process, data must be manually entered into the XML documents. Current form filling technologies are mostly for simple data entry and do not provide support for the complexity and nested structures of XML grammars. This paper presents SmartXAutofill, an intelligent data entry assistant for predicting and automating inputs for XML documents based on the contents of historical document collections in the same XML domain. SmartXAutofill incorporates an ensemble classifier, which integrates multiple internal classification algorithms into a single architecture. Each internal classifier uses approximate techniques to propose a value for an empty XML field, and, through voting, the ensemble classifier determines which value to accept. As the system operates it learns which internal classification algorithms work better for a specific XML document domain and modifies its weights (confidence) in their predictive ability. As a result, the ensemble classifier adapts itself to the specific XML domain, without the need to develop special learners for the infinite number of domains that XML users have created. We evaluated our system performance using data from eleven different XML domains. The results show that the ensemble classifier adapted itself to different XML document domains, and most of the time (for 9 out of 11 domains) produced predictive accuracies as good as or better than the best individual classifier for a domain.
{"title":"Intelligent data entry assistant for XML using ensemble learning","authors":"Danico Lee, C. Tsatsoulis","doi":"10.1145/1040830.1040856","DOIUrl":"https://doi.org/10.1145/1040830.1040856","url":null,"abstract":"XML has emerged as the primary standard of data representation and data exchange [13]. Although many software tools exist to assist the XML implementation process, data must be manually entered into the XML documents. Current form filling technologies are mostly for simple data entry and do not provide support for the complexity and nested structures of XML grammars. This paper presents SmartXAutofill, an intelligent data entry assistant for predicting and automating inputs for XML documents based on the contents of historical document collections in the same XML domain. SmartXAutofill incorporates an ensemble classifier, which integrates multiple internal classification algorithms into a single architecture. Each internal classifier uses approximate techniques to propose a value for an empty XML field, and, through voting, the ensemble classifier determines which value to accept. As the system operates it learns which internal classification algorithms work better for a specific XML document domain and modifies its weights (confidence) in their predictive ability. As a result, the ensemble classifier adapts itself to the specific XML domain, without the need to develop special learners for the infinite number of domains that XML users have created. We evaluated our system performance using data from eleven different XML domains. The results show that the ensemble classifier adapted itself to different XML document domains, and most of the time (for 9 out of 11 domains) produced predictive accuracies as good as or better than the best individual classifier for a domain.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519532","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}
H. Lieberman, A. Faaborg, Waseem Daher, J. Espinosa
A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best" candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is time-consuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as n-grams and Hidden Markov Models.We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and word-frequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that "don't make sense" from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.
{"title":"How to wreck a nice beach you sing calm incense","authors":"H. Lieberman, A. Faaborg, Waseem Daher, J. Espinosa","doi":"10.1145/1040830.1040898","DOIUrl":"https://doi.org/10.1145/1040830.1040898","url":null,"abstract":"A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the \"best\" candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is time-consuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as n-grams and Hidden Markov Models.We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and word-frequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that \"don't make sense\" from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133123299","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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