The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Joy Bose - One of the best experts on this subject based on the ideXlab platform.
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CCNC - Alternate Action Recommender System Using Recurrent Patterns of Smart Home Users
2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020Co-Authors: Prabhat Mishra, Suresh Kumar Gudla, Amogha D Shanbhag, Joy BoseAbstract:We present an alternate action recommender system for Internet of Things (IoT) smart home Users. Our system takes the data of the IoT devices of the smart home Users as input, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual User, and finally recommends the alternate possibilities of achieving the deviated actions from any of the active patterns. The derived active patterns are very specific to the Users, which are based on their usage of the IoT devices in their smart home ecosystem, thus making the recommendations personalized. In our system, we also consider the context of the smart home environment through the IoT device state and criticality of the recommendations in the User's smart home system where these parameters play crucial role. We have designed and implemented our core algorithm by modifying the SMILE-ARM algorithm to better suit the IoT devices data of the smart home ecosystem and validated it using User Trial methods and standard algorithm validation techniques.
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BigData - Enhanced Alternate Action Recommender System Using Recurrent Patterns and Fault Detection System for Smart Home Users
2019 IEEE International Conference on Big Data (Big Data), 2019Co-Authors: Prabhat Mishra, Suresh Kumar Gudla, Amogha D Shanbhag, Joy BoseAbstract:We present a fault tolerant alternate action recommender system for smart home Internet of Things (IoT) Users to enrich the User experience with uninterrupted routines and various methods to achieve the regular routines in the smart home system. Our system takes events data from the smart home IoT devices as input, performs preprocessing using the big data handling techniques to transform it to be applicable to our system, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual User, ensures those frequently used devices are up and running using our fault detection monitoring system, and then finally recommends the alternate possibilities of achieving the deviated actions. Our custom fault detection system is based on various parameters of the IoT devices and context of the smart home Users wherein the alternate recommendations given to the User are practical and useful in real time. We validated our system using User Trial methods and various validation techniques.
Kaisa Vaananenvainiomattila - One of the best experts on this subject based on the ideXlab platform.
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automated creation of mobile video remixes User Trial in three event contexts
Mobile and Ubiquitous Multimedia, 2014Co-Authors: Jarno Ojala, Sujeet Shyamsundar Mate, Igor Danilo Diego Curcio, Arto Lehtiniemi, Kaisa VaananenvainiomattilaAbstract:This paper describes a User evaluation study of automated creation of mobile video remixes in three different event contexts. The evaluation contributes to the design process of the Automatic Video Remixing System, deepening knowledge to wider usage context. The study was completed with 30 Users in three different contexts: a sports event, a music concert and a doctoral dissertation. It was discovered that Users are motivated to provide their material to the service when knowing they get an automatically created remix containing many capturers' content in return. Automatic video remixing was stated to ease the task of editing videos and to improve the quality of amateur videos. The study reveals requirements for pleasurable remix creation in different event contexts and details the User experience factors related to the content capturing, sharing, and viewing of captured content and the remixes. The results provide insights into media creation in small event-based groups.
Prabhat Mishra - One of the best experts on this subject based on the ideXlab platform.
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CCNC - Alternate Action Recommender System Using Recurrent Patterns of Smart Home Users
2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020Co-Authors: Prabhat Mishra, Suresh Kumar Gudla, Amogha D Shanbhag, Joy BoseAbstract:We present an alternate action recommender system for Internet of Things (IoT) smart home Users. Our system takes the data of the IoT devices of the smart home Users as input, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual User, and finally recommends the alternate possibilities of achieving the deviated actions from any of the active patterns. The derived active patterns are very specific to the Users, which are based on their usage of the IoT devices in their smart home ecosystem, thus making the recommendations personalized. In our system, we also consider the context of the smart home environment through the IoT device state and criticality of the recommendations in the User's smart home system where these parameters play crucial role. We have designed and implemented our core algorithm by modifying the SMILE-ARM algorithm to better suit the IoT devices data of the smart home ecosystem and validated it using User Trial methods and standard algorithm validation techniques.
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BigData - Enhanced Alternate Action Recommender System Using Recurrent Patterns and Fault Detection System for Smart Home Users
2019 IEEE International Conference on Big Data (Big Data), 2019Co-Authors: Prabhat Mishra, Suresh Kumar Gudla, Amogha D Shanbhag, Joy BoseAbstract:We present a fault tolerant alternate action recommender system for smart home Internet of Things (IoT) Users to enrich the User experience with uninterrupted routines and various methods to achieve the regular routines in the smart home system. Our system takes events data from the smart home IoT devices as input, performs preprocessing using the big data handling techniques to transform it to be applicable to our system, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual User, ensures those frequently used devices are up and running using our fault detection monitoring system, and then finally recommends the alternate possibilities of achieving the deviated actions. Our custom fault detection system is based on various parameters of the IoT devices and context of the smart home Users wherein the alternate recommendations given to the User are practical and useful in real time. We validated our system using User Trial methods and various validation techniques.
Barry Smyth - One of the best experts on this subject based on the ideXlab platform.
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HCOMP - A Game with a Purpose for Recommender Systems
2015Co-Authors: Barry Smyth, Rachael Rafter, Sam BanksAbstract:Recommender systems learn about our preferences to make targeted suggestions. In this paper we outline a novel game-with-a-purpose designed to infer preferences at scale as a side-effect of gameplay. We evaluate the utility of this data in a recommendation context as part of a small live-User Trial.
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RecSys - The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data
Proceedings of the 9th ACM Conference on Recommender Systems, 2015Co-Authors: Sam Banks, Rachael Rafter, Barry SmythAbstract:This paper describes a casual Facebook game to capture recommendation data as a side-effect of gameplay. We show how this data can be used to make successful recommendations as part of a live-User Trial.
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A comparative study of collaboration-based reputation models for social recommender systems
User Modeling and User-Adapted Interaction, 2014Co-Authors: Kevin Mcnally, Michael P. O'mahony, Barry SmythAbstract:Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to iden-tify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling User and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collabo-ration graphs, from which the reputation of Users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recom-mending relevant pages to Users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-User Trial of the system.
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Anonymous personalization in collaborative web search
Information Retrieval, 2006Co-Authors: Barry Smyth, Evelyn BalfeAbstract:We present an innovative approach to Web search, called collaborative search , that seeks to cope with the type of vague queries that are commonplace in Web search. We do this by leveraging the search behaviour of previous searchers to personalize future result-lists according to the implied preferences of a community of like-minded individuals. This technique is implemented in the I-SPY meta-search engine and we present the results of a live-User Trial which indicates that I-SPY can offer improved search performance when compared to a benchmark search engine, across a variety of performance metrics. In addition, I-SPY achieves its level of personalization while preserving the anonymity of individual Users, and we argue that this offers unique privacy benefits compared to alternative approaches to personalization.
Jarno Ojala - One of the best experts on this subject based on the ideXlab platform.
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automated creation of mobile video remixes User Trial in three event contexts
Mobile and Ubiquitous Multimedia, 2014Co-Authors: Jarno Ojala, Sujeet Shyamsundar Mate, Igor Danilo Diego Curcio, Arto Lehtiniemi, Kaisa VaananenvainiomattilaAbstract:This paper describes a User evaluation study of automated creation of mobile video remixes in three different event contexts. The evaluation contributes to the design process of the Automatic Video Remixing System, deepening knowledge to wider usage context. The study was completed with 30 Users in three different contexts: a sports event, a music concert and a doctoral dissertation. It was discovered that Users are motivated to provide their material to the service when knowing they get an automatically created remix containing many capturers' content in return. Automatic video remixing was stated to ease the task of editing videos and to improve the quality of amateur videos. The study reveals requirements for pleasurable remix creation in different event contexts and details the User experience factors related to the content capturing, sharing, and viewing of captured content and the remixes. The results provide insights into media creation in small event-based groups.
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MUM - Automated creation of mobile video remixes: User Trial in three event contexts
Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia - MUM '14, 2014Co-Authors: Jarno Ojala, Sujeet Shyamsundar Mate, Igor Danilo Diego Curcio, Arto Lehtiniemi, Kaisa Väänänen-vainio-mattilaAbstract:This paper describes a User evaluation study of automated creation of mobile video remixes in three different event contexts. The evaluation contributes to the design process of the Automatic Video Remixing System, deepening knowledge to wider usage context. The study was completed with 30 Users in three different contexts: a sports event, a music concert and a doctoral dissertation. It was discovered that Users are motivated to provide their material to the service when knowing they get an automatically created remix containing many capturers' content in return. Automatic video remixing was stated to ease the task of editing videos and to improve the quality of amateur videos. The study reveals requirements for pleasurable remix creation in different event contexts and details the User experience factors related to the content capturing, sharing, and viewing of captured content and the remixes. The results provide insights into media creation in small event-based groups.
