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When recommendation meets mobile

Jinfeng Zhuang, Tao Mei, Steven C.H. Hoi, Ying-Qing Xu, Shipeng Li
2011 Proceedings of the 13th international conference on Ubiquitous computing - UbiComp '11  
The recommended entity types and entities are relevant to both user context (past behaviors) and sensor context (time and geolocation).  ...  Specifically, it estimates the generation probability of an entity by a given user conditioned on the current context in a probabilistic framework.  ...  ACKNOWLEDGEMENTS We would like to thank Peng Bai, Zheng Chen, Xuedong Huang, Xin Lin, Xiaochuan Ni, Jian-Tao Sun, Peng Xu, Bo Zhang, and Zhimin Zhang, for their insightful discussions.  ... 
doi:10.1145/2030112.2030134 dblp:conf/huc/ZhuangMHXL11 fatcat:lmullxqsajcyfc3scojszlhh3u

Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records [article]

Aynur Guluzade, Endri Kacupaj, Maria Maleshkova
2021 arXiv   pre-print
In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical  ...  Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance.  ...  It models the relation as a translation vector between head and tail entity vectors.  ... 
arXiv:2103.11951v2 fatcat:4owc6dl3hnbqhori4eycs3pvdq

Feature-rich networks: going beyond complex network topologies

Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron, Alessandra Sala
2019 Applied Network Science  
Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models  ...  The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as  ...  Location-aware networks As discussed for the time dimension (cf.  ... 
doi:10.1007/s41109-019-0111-x fatcat:obdhovj2kffqbe6drixhw3bp5q

Advances in Algorithms for Time-Dependent Recommender Systems

Pavlos Kefalas, Yannis Manolopoulos
2014 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization  
The main factor is time that refines the final recommendation revealing relations among entities, which can increase accuracy of the proposals.  ...  Here, we argue that periodicity is a significant upcoming trend in recommender systems.  ...  Probabilistic models simulate a class of entities assigning an associated probability to each one individually. They can be divided in three types.  ... 
doi:10.1109/smap.2014.36 dblp:conf/smap/KefalasM14 fatcat:zvdibiximfh7nflluv3ussgb7y

Context-aware tensor decomposition for relation prediction in social networks

Achim Rettinger, Hendrik Wermser, Yi Huang, Volker Tresp
2012 Social Network Analysis and Mining  
While the first approach, the Context-Aware Recommendation Tensor Decomposition (CARTD), proposes an efficient optimization criterion and decomposition  ...  These models permit the inclusion of context information that is relevant for relation prediction.  ...  Context-Aware Regularized Singular Value Decomposition (CRSVD) As a next step, we consider the modeling of the probabilistic tables used in the Bayesian network.  ... 
doi:10.1007/s13278-012-0069-5 fatcat:ysogtuva65eovhcvvjbuescgsq

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems [article]

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
2021 arXiv   pre-print
To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems.  ...  It offers: a powerful, general probabilistic programming language for agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation  ...  The following example shows a partially implemented Entity prototype for a user model.  ... 
arXiv:2103.08057v1 fatcat:wgg3hbvk5nee5fjliwz24rctbm

Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition

Hendrik Wermser, Achim Rettinger, Volker Tresp
2011 2011 International Conference on Advances in Social Networks Analysis and Mining  
We argue, that a more flexible framework is needed to model and learn a greater class of recommendation scenarios where rich context is available.  ...  The task of recommending items, like movies, to users is a core feature of many social networks.  ...  CONTEXT-AWARE RECOMMENDATION TENSOR DECOMPOSITION Our goal is to model an entity together with multiple contexts.  ... 
doi:10.1109/asonam.2011.56 dblp:conf/asunam/WermserRT11 fatcat:v4t7z7kebrdnldnnbsjfo5nuqy

Situation awareness - a commander's view

G.A. Thoms
2003 Sixth International Conference of Information Fusion, 2003. Proceedings of the  
A lexicon for situation awareness is defined and the context of command in terms of organizational, operational, informational and inferential needs is examined.  ...  commander's model are explained.  ...  The author thanks Neill Smith of RLM Systems Limited for many useful discussions. References  ... 
doi:10.1109/icif.2003.177360 fatcat:ifws7h6embbp3bq5wv7ftygk6i

A Context-aware Decision Support Tool for Assessing and Mitigating Drivers of Civil Instability

Ryan S. Mullins, Adam Fouse, Robert McCormack, Stacy Lovell Pfautz
2015 Procedia Manufacturing  
Additionally, these data are inherently ambiguous, and the automated processing techniques necessary for aggregating and analyzing data may introduce further uncertainty.  ...  efforts on verifying, interpreting, and assessing the informationneeded to recommend the best course of action.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.  ... 
doi:10.1016/j.promfg.2015.07.984 fatcat:66xwnmranjdqfoirmndoqh7xli

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload.  ...  Finally, we present an exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.  ...  In real-life online platforms, the temporal patterns of user-item interactions and the underlying time-aware behavior relationships, serve as a key feature dimension in many recommender systems [Huang  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

Entity Suggestion by Example using a Conceptual Taxonomy [article]

Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang
2015 arXiv   pre-print
This paper provides a query processing method based on the relevance models between entity sets and concepts.  ...  Such entity acquisition queries can be useful in many applications such as related-entity recommendation and query expansion. A number of ESbE query processing solutions exist in the literature.  ...  In section IV, we propose two probabilistic models for our problem. Section V elaborates how we compute the models.  ... 
arXiv:1511.08996v1 fatcat:da42hhtf4rfdrcct5brej6gh6u

Cyber-Physical-Social Model for Service Recommendation in the Internet of Things

Zhijun Gao, Zhenyu Liu, Jingmin An, Lingwei Xu
2022 Mathematical Problems in Engineering  
For this, we propose a cyber-physical-social model to recommend services in IoT. The model consists of four layers: in the physical layer, the individual behavior pattern is defined.  ...  In terms of accuracy and response time, our model has outstanding advantages compared with the previous methods.  ...  [24] designed a context-aware recommendation model in a smart home. Xiao et al.  ... 
doi:10.1155/2022/9523984 fatcat:r3eigrbzvbaztiqfnzulihscaa

Entity Suggestion with Conceptual Expanation

Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
However, they are generally not aware of the concepts of query entities thus cannot be used for conceptual explanation.  ...  In this paper, we propose two probabilistic entity suggestion models and their computation solutions.  ...  We propose a series of probabilistic models and approaches for concept inference and entity suggestion based on these taxonomies.  ... 
doi:10.24963/ijcai.2017/593 dblp:conf/ijcai/ZhangXHWWW17 fatcat:w4bm4uh3dnel7ibf5skqs3hz4i

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems [article]

Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2021 arXiv   pre-print
that adds semantic awareness to the model.  ...  This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically  ...  This work puts the foundations to applying these approaches in a educational recommender using 1) a probabilistic graphical model and 2) SR values extracted from Wikipedia.  ... 
arXiv:2112.04368v1 fatcat:3hd5ebzdpnh65av2oucqompcxq

Location-Aware Personalized News Recommendation Based on Behavior and Popularity Technique

Hengane Shubham
2020 International Journal for Research in Applied Science and Engineering Technology  
Additionally, represents a dynamic technique for news recommendation, wherein each short-term and long-term user preferences are taken into consideration.  ...  Now-a-days people can read news from several sources around the world. This paper investigates a novel user profile model to express users' preferences from different aspects.  ...  A context-aware adaptive recommendation engine can fulfill the needs of journalists' daily work when retrieving timely.  ... 
doi:10.22214/ijraset.2020.27921 fatcat:arvlxufr6faczfwznrfsvzumr4
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