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A Probabilistic View of Neighborhood-Based Recommendation Methods

Jun Wang, Qiang Tang
2016 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)  
We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM.  ...  In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor.  ...  Regularized parameters (λ P , λ Q for user latent factors and latent item factors respectively) and learning rate β are set to D.  ... 
doi:10.1109/icdmw.2016.0011 dblp:conf/icdm/WangT16 fatcat:ph5j26gcnbdddhmsd3nlr5x4o4

A Probabilistic View of Neighborhood-based Recommendation Methods [article]

Jun Wang, Qiang Tang
2017 arXiv   pre-print
We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM.  ...  In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor.  ...  Regularized parameters (λ P , λ Q for user latent factors and latent item factors respectively) and learning rate β are set to D.  ... 
arXiv:1701.01250v1 fatcat:u6qocqhe6jbunkc4xndcv4vzdi

Understanding Hybrid-MOOC Effectiveness with a Collective Socio-Behavioral Model

Sabina Tomkins, Lise Getoor
2019 Zenodo  
When these relationships are not directly observed, we formulate latent constructs to capture social dynamics of learning.  ...  We propose this general socio-behavioral modeling framework as a flexible approach for including unobserved aspects of learning in meaningful ways, in order to better understand and infer student success  ...  LATENT STUDENT FACTORS AND LEARNING Another aspect of our work is modeling latent factors of student success.  ... 
doi:10.5281/zenodo.3594772 fatcat:nxnryv254renro2cmjgabmuabm

Pairwise-Ranking based Collaborative Recurrent Neural Networks for Clinical Event Prediction

Zhi Qiao, Shiwan Zhao, Cao Xiao, Xiang Li, Yong Qin, Fei Wang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
., diagnoses) from their historical EHR data is a core research task and motives a series of predictive models including deep learning.  ...  In this paper, we propose to formulate the clinical event prediction problem as an events recommendation problem.  ...  Acknowledgments The work of Fei Wang is supported by NSF IIS-1750326 and IIS-1716432.  ... 
doi:10.24963/ijcai.2018/489 dblp:conf/ijcai/QiaoZXLQW18 fatcat:mbaa7ok4rjc4fn55hi6qrtqeme

Understanding High School Students' Perceptions of Their Learning Opportunities: A Doubly Latent Approach

Denis Dumas
2018 Frontiers in Education  
Results suggested that student learning opportunities are best conceptualized as distinct but positively correlated factors, and that these doubly latent factors occurred at the student level, although  ...  This investigation aimed to inform theory-building around student learning opportunities by systematically comparing 11 theoretically plausible latent and doubly latent measurement models that differed  ...  This research was supported by the American Educational Research Association Deeper Learning Fellowship, funded by the William and Flora Hewlett Foundation.  ... 
doi:10.3389/feduc.2018.00076 fatcat:7eljhz7v4ng3hby3r6jbvbbwhm

Dynamic Cognitive Testing: What Kind of Information is Gained by Measuring Response Time and Modifiability?

Susan E. Embretson, L. Katherine Prenovost
2000 Educational and Psychological Measurement  
SLAT response time is correlated with both Factor I and Factor V.  ...  ART was administered by paper and pencil for Sample I and by com- puter for Sample II (for the dynamic version). Dynamic ART.  ... 
doi:10.1177/00131640021970943 fatcat:ajadno57ujczjp5qggql26wqgi

Exploiting Music Play Sequence for Music Recommendation

Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan Kankanhalli, Liqiang Nie
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery.  ...  ., k = 5) in the learning process and thus avoids the increase of time complexity.  ...  Influence of α The trade-off parameter α in the proposed algorithm regularizes the influence of song's similarity in latent feature learning.  ... 
doi:10.24963/ijcai.2017/511 dblp:conf/ijcai/ChengSZKN17 fatcat:wb7i5ltq2nafrmffwp7gdgrldm

Recommendation system using a deep learning and graph analysis approach [article]

Mahdi Kherad, Amir Jalaly Bidgoly
2021 arXiv   pre-print
In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph.  ...  In this paper, we have proposed a novel recommendation method based on Matrix Factorization and graph analysis methods.  ...  CONCLUSIONS In this paper, a collaborative filtering recommender method based on matrix factorization is proposed that first initializes latent factors of users and items by deep Autoencoder.  ... 
arXiv:2004.08100v8 fatcat:olpgxe5u5zg3tphqbofgdfilmu

Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
In other cases, this method will learn the user and POI factors from these two components.  ...  Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed.  ...  In this way, the learning process of latent factor model and feature extracting model mutually influence each other. For instance, Chen et al.  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e

Identifying the Academic Rising Stars [article]

Chuxu Zhang, Chuang Liu, Lu Yu, Zi-Ke Zhang, Tao Zhou
2016 arXiv   pre-print
We explore a series of factors that can drive an author to be fast-rising and design a novel impact increment ranking learning (IIRL) algorithm that leverages those factors to predict the academic rising  ...  In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest  ...  both productivity and popularity of researchers. Dong et al. [10] investigated the correlation between researchers' H-index values and their collaboration signatures. Dong et al.  ... 
arXiv:1606.05752v1 fatcat:etnu3yqjgjfubegmjyjlanyucm

Similarity measure and instance selection for collaborative filtering

Chun Zeng, Chun-Xiao Xing, Li-Zhu Zhou
2003 Proceedings of the twelfth international conference on World Wide Web - WWW '03  
However, this method suffers from two fundamental problems: sparsity and scalability. In this paper, we present our solutions for these two problems.  ...  Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce.  ...  Model-based Algorithm The model-based collaborative filtering algorithm uses the users' preferences to learn a model, which is then used for predications.  ... 
doi:10.1145/775152.775243 dblp:conf/www/ZengXZ03 fatcat:krwnszkrb5ftjh3nqlpa4453be

Location Based Services Recommendation with Budget Constraints

Bei Shi, Hao Wei
2016 International Joint Conference on Artificial Intelligence  
The paper reports the approach of our team "711" to recommend Brick-and-Mortar Store with Budget Constraints.  ...  Moreover, we extract predicative features from the train set and implement some existing methods, e.g., Xgboost.  ...  We also thank other participants for their helpful discussions in the forum and we learn a lot from them.  ... 
dblp:conf/ijcai/ShiW16 fatcat:kntnz75mlfaw7birdrnwd2uoca

Collaboration, Communication, and Connection: Collegial Support and Collective Efficacy among Health Science Teachers

Gustavo Loera, Jonathan Nakamoto, Robert Rueda, Youn Joo Oh, Cindy Beck, Carla Cherry
2013 Career and Technical Education Research  
These four factors were considered because they are major school- level factors that influence teacher collegiality and professional learning communities (e.g., Bryk, Camburn, & Louis, 1999; Bryk, Sebring  ...  Foundations for a new science of learning. Science, 325, 284-288. Mindich, D., & Lieberman, A. (2012). Building a learning community: A tale of two schools.  ... 
doi:10.5328/cter38.3.191 fatcat:3abq6jrjcbfwjifhg6nzgcikmy

Strategic alliances influence on small and medium firm performance

Cheng-Wen Lee
2007 Journal of Business Research  
This research provides managerial implications for both entrepreneurs and managers and some suggestions for future research.  ...  This study examines whether the new ventures success of small and medium enterprises (SMEs) in the biotech industry relates with the characteristics of strategic alliances.  ...  Factor loadings are the correlation of each variable and a given factor.  ... 
doi:10.1016/j.jbusres.2007.02.018 fatcat:vybxumwck5etrgpkxs7djzwqnu

Music Content Ranking using Affective Information on Social Media

R. Arun Kumar
2019 International Journal for Research in Applied Science and Engineering Technology  
But it is only easy for us humans to correlate these emotions.  ...  Subsequently, we utilize a state-of-the-art network embedding method to learn latent feature representations of users, tracks and hashtags.  ...  LITREATURE SURVEY 1) Paper Title: Applying Collaborative Filtering Techniques to Movie Search for better Ranking and Browsing a) Author: Seung-Taek Park, David M.  ... 
doi:10.22214/ijraset.2019.3367 fatcat:pwfprsm345fzzhqyxv5ro3fv44
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