Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach
Sign Up to like & getrecommendations! Published in 2018 at "Wireless Networks"
DOI: 10.1007/s11276-016-1316-x
Abstract: In recent years, the prevalent of location-based social networks contributes massive data for location recommendation. Although collaborative filtering (CF) algorithm has been widely employed for location recommendation, it suffers the data sparsity and the high… read more here.
Keywords: location; cloud model; user preferences;
User preferences and willingness to pay for in-vehicle assistance
Sign Up to like & getrecommendations! Published in 2019 at "Electronic Markets"
DOI: 10.1007/s12525-019-00330-5
Abstract: As consumers’ demand for interconnectivity and infotainment grows continuously, car manufacturers face the challenge of developing more sophisticated, user appealing and economically viable in-vehicle infotainment assistants while staying within the boundaries of their limited resources.… read more here.
Keywords: assistance; pay vehicle; preferences willingness; vehicle ... See more keywords
User preferences for privacy features in digital assistants
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DOI: 10.1007/s12525-020-00447-y
Abstract: Digital assistants (DA) perform routine tasks for users by interacting with the Internet of Things (IoT) devices and digital services. To do so, such assistants rely heavily on personal data, e.g. to provide personalized responses.… read more here.
Keywords: features digital; design privacy; digital assistants; user preferences ... See more keywords
FG-RS: Capture user fine-grained preferences through attribute information for Recommender Systems
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DOI: 10.1016/j.neucom.2021.05.068
Abstract: Abstract Recommender system uses user-item historical interactions to portray user preferences. Due to the problem of data sparseness, auxiliary information is introduced to describe user preferences, such as user/item attribute information. However, some of these… read more here.
Keywords: user fine; fine; user preferences; fine grained ... See more keywords
Knowledge graph convolutional networks with user preferences for course recommendation
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DOI: 10.1038/s41598-025-14150-5
Abstract: With the rapid growth of the internet and online education resources, the number of massive open online courses (MOOCs) has increased dramatically, making it difficult for users to find personalized courses that meet their needs.… read more here.
Keywords: graph convolutional; recommendation; knowledge graph; convolutional networks ... See more keywords
Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation
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DOI: 10.1109/access.2022.3201339
Abstract: At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then… read more here.
Keywords: information; spatiotemporal information; sequential recommendation; recommendation ... See more keywords
PRIDES: A Probabilistic Model for Recurrent User Interest Drift Identification in Session-Based Recommendation
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DOI: 10.1109/access.2025.3564460
Abstract: A Session-Based Recommendation (SBR) identifies correlations among session interactions to understand user preferences and generate appropriate recommendations. A key challenge in this context is the dynamic change in user preferences, particularly when preferences disappear and… read more here.
Keywords: ruid; session; identification; based recommendation ... See more keywords
Learning User Preferences for Complex Cobotic Tasks: Meta-Behaviors and Human Groups
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2023.3279619
Abstract: In complex tasks (beyond a single targeted controller) requiring robots to collaborate with multiple human users, two challenges arise: complex tasks are often composed of multiple behaviors which can only be evaluated as a collective… read more here.
Keywords: complex cobotic; learning user; group; preferences complex ... See more keywords
User Preferences-Based Incompatible Multiobjective Iterative Learning Tracking Control
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2025.3591608
Abstract: In multisensor systems, the output of each sensor is typically required to track a reference within a specified time frame. This situation exemplifies a multiobjective tracking problem (MOTP). By nature, MOTP involves multiple conflicting optimization… read more here.
Keywords: control; preferences based; control user; incompatible multiobjective ... See more keywords
A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH
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DOI: 10.1109/tcsvt.2019.2939282
Abstract: Dynamic adaptive video streaming over hypertext transfer protocol (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches mainly focus on optimizing the overall quality of experience (QoE) for… read more here.
Keywords: dash; dash adaptation; user preferences; qoe ... See more keywords
A Deep Dive into User's Preferences and Behavior around Mobile Phone Sharing
Sign Up to like & getrecommendations! Published in 2023 at "Proceedings of the ACM on Human-Computer Interaction"
DOI: 10.1145/3579595
Abstract: Users share their personal devices with different entities in various circumstances. While prior research shed light on the broad reasons behind the sharing of mobile phones, there is a dearth of systematic study to understand… read more here.
Keywords: phone sharing; dive user; deep dive; user preferences ... See more keywords