Kevin McCarthy, James Reilly, L. McGinty, Barry Smyth
Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.
{"title":"Experiments in dynamic critiquing","authors":"Kevin McCarthy, James Reilly, L. McGinty, Barry Smyth","doi":"10.1145/1040830.1040871","DOIUrl":"https://doi.org/10.1145/1040830.1040871","url":null,"abstract":"Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.","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":"129034131","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}
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}
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}
In this paper, we describe the development of CiteSpace as an integrated environment for identifying and tracking thematic trends in scientific literature. The goal is to simplify the process of finding not only highly cited clusters of scientific articles, but also pivotal points and trails that are likely to characterize fundamental transitions of a knowledge domain as a whole. The trails of an advancing research field are captured through a sequence of snapshots of its intellectual structure over time in the form of Pathfinder networks. These networks are subsequently merged with a localized pruning algorithm. Pivotal points in the merged network are algorithmically identified and visualized using the betweenness centrality metric. An example of finding clinical evidence associated with reducing risks of heart diseases is included to illustrate how CiteSpace could be used. The contribution of the work is its integration of various change detection algorithms and interactive visualization capabilities to simply users' tasks.
{"title":"The centrality of pivotal points in the evolution of scientific networks","authors":"Chaomei Chen","doi":"10.1145/1040830.1040859","DOIUrl":"https://doi.org/10.1145/1040830.1040859","url":null,"abstract":"In this paper, we describe the development of CiteSpace as an integrated environment for identifying and tracking thematic trends in scientific literature. The goal is to simplify the process of finding not only highly cited clusters of scientific articles, but also pivotal points and trails that are likely to characterize fundamental transitions of a knowledge domain as a whole. The trails of an advancing research field are captured through a sequence of snapshots of its intellectual structure over time in the form of Pathfinder networks. These networks are subsequently merged with a localized pruning algorithm. Pivotal points in the merged network are algorithmically identified and visualized using the betweenness centrality metric. An example of finding clinical evidence associated with reducing risks of heart diseases is included to illustrate how CiteSpace could be used. The contribution of the work is its integration of various change detection algorithms and interactive visualization capabilities to simply users' tasks.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"59 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":"116592055","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}
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}
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}
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}
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