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Discovering Hidden Contextual Factors for Implicit Feedback

Massimo Melucci, Ryen W. White
2007 International and Interdisciplinary Conference on Modeling and Using Context  
This paper presents a statistical framework based on Principal Component Analysis (PCA) for discovering the contextual factors which most strongly influence user behavior during information-seeking activities  ...  As a demonstration of the utility of PCA, we employ it in an Implicit Relevance Feedback (IRF) algorithm that observes features of user interaction, computes the feature co-variances from a few seen documents  ...  Discovering Hidden Contextual Factors Information-seeking and retrieval activities are affected by contextual factors that cannot be modeled directly.  ... 
dblp:conf/context/MelucciW07 fatcat:6wfhbonzrnbqxhiv6dazglqyki

From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users

Anh Phan Tuan, Nhat Nguyen Trong, Duong Bui Trong, Ngo Van Linh, Khoat Than
2019 Asian Conference on Machine Learning  
Previous works showed that implicit and explicit feedback has distinct properties to make a useful recommendation.  ...  Intuitively, each user has to do some implicit actions (e.g., click) before making an explicit decision (e.g., purchase).  ...  This model aims to discover the latent factors representing for each user and each item.  ... 
dblp:conf/acml/TuanTTLT19 fatcat:ugj4fc3ssnedvnrmxcvqzplvs4

Gated Attentive-Autoencoder for Content-Aware Recommendation [article]

Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu
2018 arXiv   pre-print
However, with the tremendous increase of users and items, personalized recommender systems still face several challenging problems: (1) the hardness of exploiting sparse implicit feedback; (2) the difficulty  ...  The rapid growth of Internet services and mobile devices provides an excellent opportunity to satisfy the strong demand for the personalized item or product recommendation.  ...  [1] developed an implicit coordinate descent method for feature-based factorization models.  ... 
arXiv:1812.02869v1 fatcat:yxisbveh7bcmxbgn4xy43qooiu

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, Jing Jiang
2019 Complexity  
In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback.  ...  However, most of existing methods simplify the implicit frequency feedback to binary values, which make collaborative filtering unable to accurately learn the latent user and item features.  ...  These systems alleviate the information overload problem for users, by discovering the users' hidden preferences and providing users with the personalized information, products, or services.  ... 
doi:10.1155/2019/3563674 fatcat:rc4kaow6fzg5dpppucsjdcewsy


Bhambare Monali S .
2015 International Journal of Research in Engineering and Technology  
context-rich device logs of mobile users and calculating the risk score for the app in order to generate a user friendly metric for the user to use when choosing the app.  ...  To address these two issues an approach is proposed where the apps will be classified first using the enriched contextual information from web search engine, then with the contextual features from the  ...  In implicit feedback latent semantic meaning behind the collected contextual information will be considered.  ... 
doi:10.15623/ijret.2015.0409058 fatcat:6gxg2pafxjax5lkf4cao54tgj4

Knowledge Discovery: Some Empirical Evidence and Directions for Future Research [chapter]

Meliha Handzic, Aybüke Aurum
2001 Information Age Economy  
However, despite plausible theoretical arguments for the importance of contextual knowledge, it remains unclear whether and how well can individuals discover, extract and utilise contextual knowledge contained  ...  Temperature, sunshine and tourist data was used to simulate the effects of continuous contextual factors on the sales time series.  ... 
doi:10.1007/978-3-642-57547-1_86 fatcat:mpvyww6y2jdq3dq5qckyh7xbcq


Yong Ding, Naoya Namatame, Till Riedel, Takashi Miyaki, Matthias Budde
2011 Proceedings of the 8th ACM international conference on Autonomic computing - ICAC '11  
This paper presents an energy-saving concept for home/office environments, which proposes to design a multi-layered architecture for an automatic monitoring and control.  ...  Based on wireless sensor networks and a context awareness system, the acquired data will be interpreted into different energyrelated contextual information.  ...  Different areas of research are investigating the relevant factors.  ... 
doi:10.1145/1998582.1998612 dblp:conf/icac/DingNRMB11 fatcat:tstr4vaxvnhfhk37oqg34ebrj4

Improving social game engagement on facebook through enhanced socio-contextual information

Ben Kirman, Shaun Lawson, Conor Linehan, Francesco Martino, Luciano Gamberini, Andrea Gaggioli
2010 Proceedings of the 28th international conference on Human factors in computing systems - CHI '10  
when provided as context for non task-driven game environments.  ...  In this paper we describe the results of a controlled study of a social game, Magpies, which was built on the Facebook Online Social Network (OSN) and enhanced with contextual social information in the  ...  One of the major strategies for increasing the richness of social presence has been by exposing the underlying social behaviours of group members, and making implicit factors in computermediated communication  ... 
doi:10.1145/1753326.1753589 dblp:conf/chi/KirmanLLMGG10 fatcat:xlvozispnvc5vgzpjvymxvtete

Discovering temporal hidden contexts in web sessions for user trail prediction

Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy, Toon Calders
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
We define the problem of discovering temporal hidden contexts in such way that we optimize directly the accuracy of predictive models (e.g. users' trails prediction) during the process of context acquisition  ...  We show how to learn how to apply different predictive models for each segment in this work.  ...  We would like to thank Thijs Putman from for providing the anonymized dataset used in the experimental study and for allowing to make it publicly available.  ... 
doi:10.1145/2487788.2488120 dblp:conf/www/KiselevaLPC13 fatcat:nkp5wgykoffifkbgfv4gbq4clq

Training to Optimize Learning After Traumatic Brain Injury

Elizabeth R. Skidmore
2015 Current Physical Medicine and Rehabilitation Reports  
Potential directions for future scientific inquiry are discussed throughout the review.  ...  This brief conceptual review provides an overview of learning, the impact of traumatic brain injury on explicit and implicit learning, and the current state of the science examining selected training approaches  ...  opportunities for clients to "discover" an approach to the skills they are learning or solutions to problems they are facing).  ... 
doi:10.1007/s40141-015-0081-6 pmid:26217546 pmcid:PMC4514532 fatcat:fkf4odszzfexph4q7vhcyizway

Sentiment Analysis for Product Recommendation System Using Enhanced Stochastic Learning Algorithm

S. Gayathri, K. Thyagarajan
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
For this we use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word.  ...  This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles.  ...  Existing works for Collaborative Filtering with implicit feedbacks assume that implicit feedbacks are observed as one-class positive feedbacks and missing values do not indicate the negative feedbacks.  ... 
doi:10.32628/cseit195537 fatcat:fdq55lcurvajtmd4wyxbbccgbm

COMET: Convolutional Dimension Interaction for Collaborative Filtering [article]

Zhuoyi Lin, Lei Feng, Xingzhi Guo, Yu Zhang, Rui Yin, Chee Keong Kwoh, Chi Xu
2021 arXiv   pre-print
Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and the rationality of our proposed method.  ...  Latent factor models play a dominant role among recommendation techniques.  ...  We focus on recommendation from implicit feedback as same as ConvNCF [14] .  ... 
arXiv:2007.14129v5 fatcat:qrejcxuv7zhmdmqnq7kutsxbyi

A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation [article]

Qiang Cui, Shu Wu, Yan Huang, Liang Wang
2018 arXiv   pre-print
We fuse the current hidden state and a contextual hidden state built by the attention mechanism, which leads to a more suitable user's overall interest.  ...  Sequential recommendation is one of fundamental tasks for Web applications. Previous methods are mostly based on Markov chains with a strong Markov assumption.  ...  . • BPR: This method refers to the BPR-MF for implicit feedback (Rendle et al. 2009) .  ... 
arXiv:1711.05114v3 fatcat:c4nmkzhenfbudew43qvfj6ltdq

Incorporating Contextual Cues into Electronic Repositories

Jie-Mein Goh, Danny C. C. Poo, Klarissa Chang
2004 Pacific Asia Conference on Information Systems  
repositories thereby facilitating users to discover knowledge.  ...  In this paper, we present a framework that helps to incorporate contextual cues in information systems.  ...  In addition, the computational cost of such implicit ratings must be at best hidden away from the user.  ... 
dblp:conf/pacis/GohPC04 fatcat:mfhgyhejcndopgcr2eafds3xbm

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence [article]

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
2018 arXiv   pre-print
However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling non-linear user-POI interactions from implicit feedback  ...  The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services.  ...  Weighted Loss for Implicit Feedback In POI recommendation, check-in data is treated as implicit feedback.  ... 
arXiv:1809.10770v1 fatcat:4jbtumufrfbefc436kzqxufeku
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