Oliver S. Schneider, Karon E Maclean, Kerem Altun, Idin Karuei, Michael M. A. Wu
Persuasive technology is now mobile and context-aware. Intelligent analysis of accelerometer signals in smartphones and other specialized devices has recently been used to classify activity (e.g., distinguishing walking from cycling) to encourage physical activity, sustainable transport, and other social goals. Unfortunately, results vary drastically due to differences in methodology and problem domain. The present report begins by structuring a survey of current work within a new framework, which highlights comparable characteristics between studies; this provided a tool by which we and others can understand the current state-of-the art and guide research towards existing gaps. We then present a new user study, positioned in an identified gap, that pushes limits of current success with a challenging problem: the real-time classification of 15 similar and novel gaits suitable for several persuasive application areas, focused on the growing phenomenon of exercise games. We achieve a mean correct classification rate of 78.1% of all 15 gaits with a minimal amount of personalized training of the classifier for each participant when carried in any of 6 different carrying locations (not known a priori). When narrowed to a subset of four gaits and one location that is known, this improves to means of 92.2% with and 87.2% without personalization. Finally, we group our findings into design guidelines and quantify variation in accuracy when an algorithm is trained for a known location and participant.
{"title":"Real-time gait classification for persuasive smartphone apps: structuring the literature and pushing the limits","authors":"Oliver S. Schneider, Karon E Maclean, Kerem Altun, Idin Karuei, Michael M. A. Wu","doi":"10.1145/2449396.2449418","DOIUrl":"https://doi.org/10.1145/2449396.2449418","url":null,"abstract":"Persuasive technology is now mobile and context-aware. Intelligent analysis of accelerometer signals in smartphones and other specialized devices has recently been used to classify activity (e.g., distinguishing walking from cycling) to encourage physical activity, sustainable transport, and other social goals. Unfortunately, results vary drastically due to differences in methodology and problem domain. The present report begins by structuring a survey of current work within a new framework, which highlights comparable characteristics between studies; this provided a tool by which we and others can understand the current state-of-the art and guide research towards existing gaps. We then present a new user study, positioned in an identified gap, that pushes limits of current success with a challenging problem: the real-time classification of 15 similar and novel gaits suitable for several persuasive application areas, focused on the growing phenomenon of exercise games. We achieve a mean correct classification rate of 78.1% of all 15 gaits with a minimal amount of personalized training of the classifier for each participant when carried in any of 6 different carrying locations (not known a priori). When narrowed to a subset of four gaits and one location that is known, this improves to means of 92.2% with and 87.2% without personalization. Finally, we group our findings into design guidelines and quantify variation in accuracy when an algorithm is trained for a known location and participant.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88826307","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}
Lauren Cairco, Toni Bloodworth, L. Hodges, N. Meehan, Arlene Johnson
One of the most common clinical education methods for teaching patient interaction skills to nursing students is role-playing established scenarios with their classmates. Unfortunately, this is far from simulating real world experiences that they will soon face, and does not provide the immediate, impartial feedback necessary for interviewing skills development. We present a system for Scaffolded Interviews Developed by Nurses In Education (SIDNIE) that supports baccalaureate nursing education by providing multiple guided interview practice sessions with virtual characters. Our scenario depicts a mother who has brought in her five year old child to the clinic. In this paper we describe our system and report on a preliminary usability evaluation conducted with nursing students.
{"title":"SIDNIE: scaffolded interviews developed by nurses in education","authors":"Lauren Cairco, Toni Bloodworth, L. Hodges, N. Meehan, Arlene Johnson","doi":"10.1145/2449396.2449447","DOIUrl":"https://doi.org/10.1145/2449396.2449447","url":null,"abstract":"One of the most common clinical education methods for teaching patient interaction skills to nursing students is role-playing established scenarios with their classmates. Unfortunately, this is far from simulating real world experiences that they will soon face, and does not provide the immediate, impartial feedback necessary for interviewing skills development. We present a system for Scaffolded Interviews Developed by Nurses In Education (SIDNIE) that supports baccalaureate nursing education by providing multiple guided interview practice sessions with virtual characters. Our scenario depicts a mother who has brought in her five year old child to the clinic. In this paper we describe our system and report on a preliminary usability evaluation conducted with nursing students.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85449773","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 recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.
{"title":"Functionality-based clustering using short textual description: helping users to find apps installed on their mobile device","authors":"Dai Lulu, T. Kuflik","doi":"10.1145/2449396.2449434","DOIUrl":"https://doi.org/10.1145/2449396.2449434","url":null,"abstract":"In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85830297","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 want to translate the Web into every major language: every webpage, every video, and, yes, even Justin Bieber's tweets. With its content split up into hundreds of languages -- and with over 50% of it in English -- most of the Web is inaccessible to most people in the world. This problem is pressing, now more than ever, with millions of people from China, Russia, Latin America and other quickly developing regions entering the Web. In this talk, I introduce my new project, called Duolingo, which aims at breaking this language barrier, and thus making the Web truly "world wide." We have all seen how systems such as Google Translate are improving every day at translating the gist of things written in other languages. Unfortunately, they are not yet accurate enough for my purpose: Even when what they spit out is intelligible, it's so badly written that I can't read more than a few lines before getting a headache. With Duolingo, our goal is to encourage people, like you and me, to translate the Web into their native languages.
{"title":"Duolingo: learn a language for free while helping to translate the web","authors":"Luis von Ahn","doi":"10.1145/2449396.2449398","DOIUrl":"https://doi.org/10.1145/2449396.2449398","url":null,"abstract":"I want to translate the Web into every major language: every webpage, every video, and, yes, even Justin Bieber's tweets. With its content split up into hundreds of languages -- and with over 50% of it in English -- most of the Web is inaccessible to most people in the world. This problem is pressing, now more than ever, with millions of people from China, Russia, Latin America and other quickly developing regions entering the Web. In this talk, I introduce my new project, called Duolingo, which aims at breaking this language barrier, and thus making the Web truly \"world wide.\"\u0000 We have all seen how systems such as Google Translate are improving every day at translating the gist of things written in other languages. Unfortunately, they are not yet accurate enough for my purpose: Even when what they spit out is intelligible, it's so badly written that I can't read more than a few lines before getting a headache.\u0000 With Duolingo, our goal is to encourage people, like you and me, to translate the Web into their native languages.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89489260","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}
While journalism is evolving toward a rather open-minded participatory paradigm, social media presents overwhelming streams of data that make it difficult to identify the information of a journalist's interest. Given the increasing interest of journalists in broadening and democratizing news by incorporating social media sources, we have developed TweetGathering, a prototype tool that provides curated and contextualized access to news stories on Twitter. This tool was built with the aim of assisting journalists both with gathering and with researching news stories as users comment on them. Five journalism professionals who tested the tool found helpful characteristics that could assist them with gathering additional facts on breaking news, as well as facilitating discovery of potential information sources such as witnesses in the geographical locations of news.
{"title":"Curating and contextualizing Twitter stories to assist with social newsgathering","authors":"A. Zubiaga, Heng Ji, Kevin Knight","doi":"10.1145/2449396.2449424","DOIUrl":"https://doi.org/10.1145/2449396.2449424","url":null,"abstract":"While journalism is evolving toward a rather open-minded participatory paradigm, social media presents overwhelming streams of data that make it difficult to identify the information of a journalist's interest. Given the increasing interest of journalists in broadening and democratizing news by incorporating social media sources, we have developed TweetGathering, a prototype tool that provides curated and contextualized access to news stories on Twitter. This tool was built with the aim of assisting journalists both with gathering and with researching news stories as users comment on them. Five journalism professionals who tested the tool found helpful characteristics that could assist them with gathering additional facts on breaking news, as well as facilitating discovery of potential information sources such as witnesses in the geographical locations of news.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89628242","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}
Recent research has demonstrated that human-generated reward signals can be effectively used to train agents to perform a range of reinforcement learning tasks. Such tasks are either episodic - i.e., conducted in unconnected episodes of activity that often end in either goal or failure states - or continuing - i.e., indefinitely ongoing. Another point of difference is whether the learning agent highly discounts the value of future reward - a myopic agent - or conversely values future reward appreciably. In recent work, we found that previous approaches to learning from human reward all used myopic valuation [7]. This study additionally provided evidence for the desirability of myopic valuation in task domains that are both goal-based and episodic. In this paper, we conduct three user studies that examine critical assumptions of our previous research: task episodicity, optimal behavior with respect to a Markov Decision Process, and lack of a failure state in the goal-based task. In the first experiment, we show that converting a simple episodic task to non-episodic (i.e., continuing) task resolves some theoretical issues present in episodic tasks with generally positive reward and - relatedly - enables highly successful learning with non-myopic valuation in multiple user studies. The primary learning algorithm in this paper, which we call "VI-TAMER", is it the first algorithm to successfully learn non-myopically from human-generated reward; we also empirically show that such non-myopic valuation facilitates higher-level understanding of the task. Anticipating the complexity of real-world problems, we perform two subsequent user studies - one with a failure state added - that compare (1) learning when states are updated asynchronously with local bias - i.e., states quickly reachable from the agent's current state are updated more often than other states - to (2) learning with the fully synchronous sweeps across each state in the VI-TAMER algorithm. With these locally biased updates, we find that the general positivity of human reward creates problems even for continuing tasks, revealing a distinct research challenge for future work.
{"title":"Learning non-myopically from human-generated reward","authors":"W. B. Knox, P. Stone","doi":"10.1145/2449396.2449422","DOIUrl":"https://doi.org/10.1145/2449396.2449422","url":null,"abstract":"Recent research has demonstrated that human-generated reward signals can be effectively used to train agents to perform a range of reinforcement learning tasks. Such tasks are either episodic - i.e., conducted in unconnected episodes of activity that often end in either goal or failure states - or continuing - i.e., indefinitely ongoing. Another point of difference is whether the learning agent highly discounts the value of future reward - a myopic agent - or conversely values future reward appreciably. In recent work, we found that previous approaches to learning from human reward all used myopic valuation [7]. This study additionally provided evidence for the desirability of myopic valuation in task domains that are both goal-based and episodic.\u0000 In this paper, we conduct three user studies that examine critical assumptions of our previous research: task episodicity, optimal behavior with respect to a Markov Decision Process, and lack of a failure state in the goal-based task. In the first experiment, we show that converting a simple episodic task to non-episodic (i.e., continuing) task resolves some theoretical issues present in episodic tasks with generally positive reward and - relatedly - enables highly successful learning with non-myopic valuation in multiple user studies. The primary learning algorithm in this paper, which we call \"VI-TAMER\", is it the first algorithm to successfully learn non-myopically from human-generated reward; we also empirically show that such non-myopic valuation facilitates higher-level understanding of the task. Anticipating the complexity of real-world problems, we perform two subsequent user studies - one with a failure state added - that compare (1) learning when states are updated asynchronously with local bias - i.e., states quickly reachable from the agent's current state are updated more often than other states - to (2) learning with the fully synchronous sweeps across each state in the VI-TAMER algorithm. With these locally biased updates, we find that the general positivity of human reward creates problems even for continuing tasks, revealing a distinct research challenge for future work.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77549724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. V. Zezschwitz, Anton Koslow, A. D. Luca, H. Hussmann
Most of today's smartphones and tablet computers feature touchscreens as the main way of interaction. By using these touchscreens, oily residues of the users' fingers, smudge, remain on the device's display. As this smudge can be used to deduce formerly entered data, authentication tokens are jeopardized. Most notably, grid-based authentication methods, like the Android pattern scheme are prone to such attacks. Based on a thorough development process using low fidelity and high fidelity prototyping, we designed three graphic-based authentication methods in a way to leave smudge traces, which are not easy to interpret. We present one grid-based and two randomized graphical approaches and report on two user studies that we performed to prove the feasibility of these concepts. The authentication schemes were compared to the widely used Android pattern authentication and analyzed in terms of performance, usability and security. The results indicate that our concepts are significantly more secure against smudge attacks while keeping high input speed.
{"title":"Making graphic-based authentication secure against smudge attacks","authors":"E. V. Zezschwitz, Anton Koslow, A. D. Luca, H. Hussmann","doi":"10.1145/2449396.2449432","DOIUrl":"https://doi.org/10.1145/2449396.2449432","url":null,"abstract":"Most of today's smartphones and tablet computers feature touchscreens as the main way of interaction. By using these touchscreens, oily residues of the users' fingers, smudge, remain on the device's display. As this smudge can be used to deduce formerly entered data, authentication tokens are jeopardized. Most notably, grid-based authentication methods, like the Android pattern scheme are prone to such attacks.\u0000 Based on a thorough development process using low fidelity and high fidelity prototyping, we designed three graphic-based authentication methods in a way to leave smudge traces, which are not easy to interpret. We present one grid-based and two randomized graphical approaches and report on two user studies that we performed to prove the feasibility of these concepts. The authentication schemes were compared to the widely used Android pattern authentication and analyzed in terms of performance, usability and security. The results indicate that our concepts are significantly more secure against smudge attacks while keeping high input speed.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85796904","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}
J. Fischer, S. Ramchurn, Michael A. Osborne, Oliver Parson, T. D. Huynh, Muddasser Alam, Nadia Pantidi, Stuart Moran, K. Bachour, S. Reece, Enrico Costanza, T. Rodden, N. Jennings
We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
{"title":"Recommending energy tariffs and load shifting based on smart household usage profiling","authors":"J. Fischer, S. Ramchurn, Michael A. Osborne, Oliver Parson, T. D. Huynh, Muddasser Alam, Nadia Pantidi, Stuart Moran, K. Bachour, S. Reece, Enrico Costanza, T. Rodden, N. Jennings","doi":"10.1145/2449396.2449446","DOIUrl":"https://doi.org/10.1145/2449396.2449446","url":null,"abstract":"We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86804224","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}
Chiew Seng Sean Tan, Johannes Schöning, K. Luyten, K. Coninx
Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.
{"title":"Informing intelligent user interfaces by inferring affective states from body postures in ubiquitous computing environments","authors":"Chiew Seng Sean Tan, Johannes Schöning, K. Luyten, K. Coninx","doi":"10.1145/2449396.2449427","DOIUrl":"https://doi.org/10.1145/2449396.2449427","url":null,"abstract":"Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81643634","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}
Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik
With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a graph-based approach to a recommender system that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on the MovieLens dataset demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.
{"title":"Tailoring recommendations to groups of users: a graph walk-based approach","authors":"Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik","doi":"10.1145/2449396.2449401","DOIUrl":"https://doi.org/10.1145/2449396.2449401","url":null,"abstract":"With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a graph-based approach to a recommender system that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on the MovieLens dataset demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85493496","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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