The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.
{"title":"Practical Representation Learning for Recommender Systems","authors":"O. Zakharchuk","doi":"10.1145/3176349.3176900","DOIUrl":"https://doi.org/10.1145/3176349.3176900","url":null,"abstract":"The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"220 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120838938","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 aim of this study is to identify social effects on task-based information seeking behavior. Task has been studied for understanding information seeking behavior in relation to task properties and task performers» characteristics. However, there has been little attention to social contexts of task. This work focuses on social aspects of task performance and information seeking behavior by analyzing effects of a social context in which task is generated and conducted on cognition of individual performers. A novel theoretical framework has been designed based on literature on information science and sociology. In the future, data will be collected using self-recorded diaries and subsequent in-depth interviews.
{"title":"Exploring the Effects of Social Contexts on Task-Based Information Seeking Behavior","authors":"Eun Youp Rha","doi":"10.1145/3176349.3176356","DOIUrl":"https://doi.org/10.1145/3176349.3176356","url":null,"abstract":"The aim of this study is to identify social effects on task-based information seeking behavior. Task has been studied for understanding information seeking behavior in relation to task properties and task performers» characteristics. However, there has been little attention to social contexts of task. This work focuses on social aspects of task performance and information seeking behavior by analyzing effects of a social context in which task is generated and conducted on cognition of individual performers. A novel theoretical framework has been designed based on literature on information science and sociology. In the future, data will be collected using self-recorded diaries and subsequent in-depth interviews.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121454008","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}
Memory augmentation is the process of providing human memory with information that facilitates and complements the recall of an event in a person»s past. Recently, there has been a lot of attention on processing the content of meetings for later reuse, such as reviewing a meeting for supporting failing memories, keeping in mind key issues, verification, etc. That is due to the fact that meetings are essential for sharing knowledge in organizations. In this paper, we propose four novel time-series methods for predicting the topics that one should review in preparation for a next meeting. The predicted/recommended topics can be reviewed by a user as a memory augmentation process to facilitate recall of key points of a previous meeting. With the growing number of meetings at an organization that one may attend weekly and with the growing number of topics discussed, forgetting past meetings becomes eminent, hence recommending certain topics to the user in order to prepare the user for a future meeting is beneficial and important. Our experimental results on real-world data, demonstrate that our methods significantly outperform a state-of-the-art Hidden Markov Model baseline. This indicates the efficacy of our proposed methods for modeling semantics in temporal data.
{"title":"Augmentation of Human Memory: Anticipating Topics that Continue in the Next Meeting","authors":"Seyed Ali Bahrainian, F. Crestani","doi":"10.1145/3176349.3176399","DOIUrl":"https://doi.org/10.1145/3176349.3176399","url":null,"abstract":"Memory augmentation is the process of providing human memory with information that facilitates and complements the recall of an event in a person»s past. Recently, there has been a lot of attention on processing the content of meetings for later reuse, such as reviewing a meeting for supporting failing memories, keeping in mind key issues, verification, etc. That is due to the fact that meetings are essential for sharing knowledge in organizations. In this paper, we propose four novel time-series methods for predicting the topics that one should review in preparation for a next meeting. The predicted/recommended topics can be reviewed by a user as a memory augmentation process to facilitate recall of key points of a previous meeting. With the growing number of meetings at an organization that one may attend weekly and with the growing number of topics discussed, forgetting past meetings becomes eminent, hence recommending certain topics to the user in order to prepare the user for a future meeting is beneficial and important. Our experimental results on real-world data, demonstrate that our methods significantly outperform a state-of-the-art Hidden Markov Model baseline. This indicates the efficacy of our proposed methods for modeling semantics in temporal data.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284625","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}
Why do we listen to music? This question has as many answers as there are people, which may vary by time of day, and the activity of the listener. We envision a contextual music search and recommendation system, which could suggest appropriate music to the user in the current context. As an important step in this direction, we set out to understand what are the users» intents for listening to music, and how they relate to common daily activities. To accomplish this, we conduct and analyze a survey of why and when people of different ages and in different countries listen to music. The resulting categories of common musical intents, and the associations of intents and activities, could be helpful for guiding the development and evaluation of contextual music recommendation systems.
{"title":"Understanding Music Listening Intents During Daily Activities with Implications for Contextual Music Recommendation","authors":"Sergey Volokhin, Eugene Agichtein","doi":"10.1145/3176349.3176885","DOIUrl":"https://doi.org/10.1145/3176349.3176885","url":null,"abstract":"Why do we listen to music? This question has as many answers as there are people, which may vary by time of day, and the activity of the listener. We envision a contextual music search and recommendation system, which could suggest appropriate music to the user in the current context. As an important step in this direction, we set out to understand what are the users» intents for listening to music, and how they relate to common daily activities. To accomplish this, we conduct and analyze a survey of why and when people of different ages and in different countries listen to music. The resulting categories of common musical intents, and the associations of intents and activities, could be helpful for guiding the development and evaluation of contextual music recommendation systems.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128042583","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}
Having become integral to daily life, smartphones become a main tool in addressing daily information needs. Smartphones provide immediate and ubiquitous access to the internet. Mobile apps are becoming popular resources for the general public and patients to obtain health-related information and to self-manage their health. Little is known about patients' needs for information in the context of their phone use. Thus, this study investigates the context of emergence of information needs of diabetes patients using smartphones. This study focuses on the chronic disease type 2 diabetes because patients with this condition are required to take an active role in managing their condition on a daily basis. This study employs employ a web-based survey using the critical incident technique. This study has theoretical significance and practical implications. Information needs should be conceptualized in the contexts that give rise to them. This study will enrich our understanding of multi-faceted information needs related to chronic disease self-care in daily life. Understanding the information needs of diabetes patients and the contexts for the needs is necessary to help researchers and designers develop mobile services to satisfy patients' needs and requirements.
{"title":"Contextualizing Information Needs of Patients with Chronic Conditions Using Smartphones","authors":"Henna Kim","doi":"10.1145/3176349.3176352","DOIUrl":"https://doi.org/10.1145/3176349.3176352","url":null,"abstract":"Having become integral to daily life, smartphones become a main tool in addressing daily information needs. Smartphones provide immediate and ubiquitous access to the internet. Mobile apps are becoming popular resources for the general public and patients to obtain health-related information and to self-manage their health. Little is known about patients' needs for information in the context of their phone use. Thus, this study investigates the context of emergence of information needs of diabetes patients using smartphones. This study focuses on the chronic disease type 2 diabetes because patients with this condition are required to take an active role in managing their condition on a daily basis. This study employs employ a web-based survey using the critical incident technique. This study has theoretical significance and practical implications. Information needs should be conceptualized in the contexts that give rise to them. This study will enrich our understanding of multi-faceted information needs related to chronic disease self-care in daily life. Understanding the information needs of diabetes patients and the contexts for the needs is necessary to help researchers and designers develop mobile services to satisfy patients' needs and requirements.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508623","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}
Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, M. S. Pera
Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children»s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK»s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK»s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children»s reading skills.
{"title":"Looking for the Movie Seven or Sven from the Movie Frozen?: A Multi-perspective Strategy for Recommending Queries for Children","authors":"Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, M. S. Pera","doi":"10.1145/3176349.3176379","DOIUrl":"https://doi.org/10.1145/3176349.3176379","url":null,"abstract":"Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children»s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK»s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK»s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children»s reading skills.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124505295","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}
Relevance judgments are essential for designing information retrieval systems. Traditionally, judgments have been gathered via desktop interfaces. However, with the rise in popularity of smaller devices for information access, it has become imperative to investigate whether desktop based judgments are different from mobile judgments. Recently, user effort and document usefulness have also emerged as important dimensions to optimize and evaluate information retrieval systems. Since existing work is limited to desktops, it remains to be seen how these judgments are affected by user»s search device. In this paper, we address these shortcomings by collecting and analyzing relevance, usefulness and effort judgments on mobiles and desktops. Analysis of these judgments shows high agreement rate between desktop and mobile judges for relevance, followed by usefulness and findability. We also found that desktop judges are likely to spend more time and examine non-relevant/not-useful/difficult documents in greater depth compared to mobile judges. Based on our findings, we suggest that relevance judgments should be gathered via desktops and effort judgments should be collected on each device independently.
{"title":"Study of Relevance and Effort across Devices","authors":"Manisha Verma, Emine Yilmaz, Nick Craswell","doi":"10.1145/3176349.3176888","DOIUrl":"https://doi.org/10.1145/3176349.3176888","url":null,"abstract":"Relevance judgments are essential for designing information retrieval systems. Traditionally, judgments have been gathered via desktop interfaces. However, with the rise in popularity of smaller devices for information access, it has become imperative to investigate whether desktop based judgments are different from mobile judgments. Recently, user effort and document usefulness have also emerged as important dimensions to optimize and evaluate information retrieval systems. Since existing work is limited to desktops, it remains to be seen how these judgments are affected by user»s search device. In this paper, we address these shortcomings by collecting and analyzing relevance, usefulness and effort judgments on mobiles and desktops. Analysis of these judgments shows high agreement rate between desktop and mobile judges for relevance, followed by usefulness and findability. We also found that desktop judges are likely to spend more time and examine non-relevant/not-useful/difficult documents in greater depth compared to mobile judges. Based on our findings, we suggest that relevance judgments should be gathered via desktops and effort judgments should be collected on each device independently.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117233798","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 explore the gap between 1) statistically significant relationships between task and browsing behavior and 2) predicting task type from such behaviors. Previous literature has shown relationships between Web browsing behavior and person»s corresponding search task. We find statistically significant browser features for detecting task - comparing the features to previous literature - and apply this knowledge to task classification of search sessions. Even though significant features improve prediction over baselines, it is not by much. We suggest that a more subtle treatment of such features should go beyond statistical significance. In some cases, considering personal patterns may be required for effective prediction.
{"title":"The Paradox of Personalization: Does Task Prediction Require Individualized Models?","authors":"M. Mitsui, Jiqun Liu, C. Shah","doi":"10.1145/3176349.3176887","DOIUrl":"https://doi.org/10.1145/3176349.3176887","url":null,"abstract":"We explore the gap between 1) statistically significant relationships between task and browsing behavior and 2) predicting task type from such behaviors. Previous literature has shown relationships between Web browsing behavior and person»s corresponding search task. We find statistically significant browser features for detecting task - comparing the features to previous literature - and apply this knowledge to task classification of search sessions. Even though significant features improve prediction over baselines, it is not by much. We suggest that a more subtle treatment of such features should go beyond statistical significance. In some cases, considering personal patterns may be required for effective prediction.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"140 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122947658","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 conversational nature of intelligent personal assistants (IPAs) has the potential to trigger personification tendencies in users, which in turn can translate into consumer loyalty and satisfaction. We conducted a study of Amazon Alexa usage and explored the manifestations and possible correlates of users' personification of Alexa. The data were collected via diary instrument from nineteen Alexa users over four days. Less than half of the participants reported personification behaviors. Most of the personification reports can be characterized as mindless politeness (saying 'thank you' and 'please' to Alexa). Two participants expressed deeper personification by confessing their love and reprimanding Alexa. A new study is underway to understand whether expressions of personifications are caused by users' emotional attachments or skepticism about technology's intelligence.
{"title":"Personification of the Amazon Alexa: BFF or a Mindless Companion","authors":"Irene Lopatovska, Harriet Williams","doi":"10.1145/3176349.3176868","DOIUrl":"https://doi.org/10.1145/3176349.3176868","url":null,"abstract":"The conversational nature of intelligent personal assistants (IPAs) has the potential to trigger personification tendencies in users, which in turn can translate into consumer loyalty and satisfaction. We conducted a study of Amazon Alexa usage and explored the manifestations and possible correlates of users' personification of Alexa. The data were collected via diary instrument from nineteen Alexa users over four days. Less than half of the participants reported personification behaviors. Most of the personification reports can be characterized as mindless politeness (saying 'thank you' and 'please' to Alexa). Two participants expressed deeper personification by confessing their love and reprimanding Alexa. A new study is underway to understand whether expressions of personifications are caused by users' emotional attachments or skepticism about technology's intelligence.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197508","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}
Horatiu Bota, Adam Fourney, S. Dumais, T. L. Religa, Robert Rounthwaite
Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed.
{"title":"Characterizing Search Behavior in Productivity Software","authors":"Horatiu Bota, Adam Fourney, S. Dumais, T. L. Religa, Robert Rounthwaite","doi":"10.1145/3176349.3176395","DOIUrl":"https://doi.org/10.1145/3176349.3176395","url":null,"abstract":"Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116679704","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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