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Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Madhuri Potey, Pradeep K Sinha
2015 International journal of Web & Semantic Technology  
Machine learning and Soft computing Techniques have the ability to handle the uncertainty and are extensively being used for user modeling purpose.  ...  This paper reviews various approaches of user modeling and critically analyzes the machine learning and soft computing techniques that have successfully captured and formally modelled the human behavior  ...  Finally we discussed the machine learning and soft computing techniques along with how the knowledge about user is formalized and represented (using KR formalisms) and reasoned (classification, Recommendation  ... 
doi:10.5121/ijwest.2015.6104 fatcat:q43eezsnr5epredqr7xuhid3i4

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

Ninghua Sun, Tao Chen, Wenshan Guo, Longya Ran
2021 Applied Sciences  
A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback.  ...  We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system.  ...  Fast Matrix Factorization for Online Recommendation with Implicit Feedback.  ... 
doi:10.3390/app112412119 fatcat:2ubrplfnjzc63gppy4uhcyslgm

A Survey on Reinforcement Learning for Recommender Systems [article]

Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
2022 arXiv   pre-print
In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years.  ...  Empirical results show that RL-based recommendation methods often surpass most of supervised learning methods, owing to the interactive nature and autonomous learning ability.  ...  To learn the user's preferences from sparse user feedback, [58] proposes a KG-enhanced Q-learning model for interactive recommender systems.  ... 
arXiv:2109.10665v2 fatcat:wx5ghn66hzg7faxee54jf7gspq

GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback [article]

Wei Yinwei, Wang Xiang, Nie Liqiang, He Xiangnan, Chua Tat-Seng
2021 arXiv   pre-print
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks.  ...  Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with less-interested items occur in implicit feedback (say, a user views micro-videos accidentally  ...  Nevertheless, the method is designed for the recommender system with explicit feedback data (i.e. ratings). Towards the implicit feedback, Ying et al.  ... 
arXiv:2111.02036v1 fatcat:5mzoey6vqbardgors7sbo7tc74

A Study of the Effect of Knowledge Management on Organizational Culture and Organizational Effectiveness in Medicine and Health Sciences

Hongmei Tang
2017 Eurasia Journal of Mathematics, Science and Technology Education  
At the final stage of this study, we proposed our recommendations for the health care industry with an expectation that these could serve as the direction for stipulating health care policies in the future  ...  The usage of medical resources can meet required overall medial quality via the implementation of knowledge managements.  ...  Jacobsen et al. (2014) proposed that the major function and objective of HRM is to get members equipped with the behaviors and attitudes that comply with organizational expectations via managements systems  ... 
doi:10.12973/eurasia.2017.00700a fatcat:kdih3pseobgdtp2h4hj4c4juou

Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation [article]

Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J.Senthilnath, Chi Xu, Chee-Keong Kwoh
2022 arXiv   pre-print
However, most existing re-ranking solutions only learn from implicit feedback with a shared prediction model, which regrettably ignore inter-item relationships under diverse user intentions.  ...  We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.  ...  for candidate items, which are learned from implicit feedback.  ... 
arXiv:2201.05333v2 fatcat:z7c7rbfn6bdvbdjrx6bvw2xk6i

Battlefield Situation Information Recommendation Based on Recall-Ranking

Chunhua Zhou, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo
2020 Intelligent Automation and Soft Computing  
While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative  ...  feedback.  ...  Funding Statement: The National Natural Science Fund of China (No. 61773399) and the National Social Science Fund of China (No. 14gj003-073) supported our work with RMB 200,000 and RMB 100,000 respectively  ... 
doi:10.32604/iasc.2020.011757 fatcat:pb5brbqgprdubbdlhksmtajwee

Explaining Recommendations by Means of User Reviews

Tim Donkers, Benedikt Loepp, Jürgen Ziegler
2018 International Conference on Intelligent User Interfaces  
In this paper, we describe a set of developments we undertook to couple such textual content with common recommender techniques.  ...  The field of recommender systems has seen substantial progress in recent years in terms of algorithmic sophistication and quality of recommendations as measured by standard accuracy metrics.  ...  TagMF enhances a standard matrix factorization [12] algorithm with tags users provided for the items.  ... 
dblp:conf/iui/DonkersL018 fatcat:ioxdf4yb7rgw3emj364kkmpbeu

Community-based Cyberreading for Information Understanding

Zhuoren Jiang, Xiaozhong Liu, Liangcai Gao, Zhi Tang
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
For each cluster, a learning to rank model will be generated to recommend readers' communitized resources (i.e., videos, slides, and wikis) to help them understand the target publication.  ...  For virtual collaboration, instead of pushing readers to communicate with others, we cluster readers based on their estimated information needs.  ...  As Table 2 shows, the recommendation based on RPF (p<0.001) and RBF with Max Entropy training (p<0.05) outperforms globe learning-to-rank (baseline) OER-recommendation method for all the metrics.  ... 
doi:10.1145/2911451.2914744 dblp:conf/sigir/JiangLGT16 fatcat:5hvuzercjrfexkm5zjqcn6ilt4

Collaborative Memory Network for Recommendation Systems

Travis Ebesu, Bin Shen, Yi Fang
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms.  ...  The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood  ...  implicit feedback.  ... 
doi:10.1145/3209978.3209991 dblp:conf/sigir/EbesuSF18 fatcat:dixbrvd6o5gd5kqziruxyn2hb4

Sequential Recommendation with Graph Neural Networks [article]

Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, Yong Li
2021 arXiv   pre-print
In this work, we propose a graph neural network model called SURGE (short for SeqUential Recommendation with Graph neural nEtworks) to address these two issues.  ...  Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation.  ...  Users may interact with many items with implicit feedback, such as clicks and watches.  ... 
arXiv:2106.14226v1 fatcat:c6inmah6qrh2vlsybo6viftwci

Personalized Service Recommendation With Mashup Group Preference in Heterogeneous Information Network

Fenfang Xie, Liang Chen, Dongding Lin, Zibin Zheng, Xiaola Lin
2019 IEEE Access  
Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model.  ...  First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings.  ...  Further, we adopt a group preference based Bayesian personalized ranking algorithm to learn the recommendation model with implicit feedback data. • We conduct various experiments on a real-world dataset  ... 
doi:10.1109/access.2019.2894822 fatcat:3c2ijwlfv5arzd6apwkmushs3u

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation [article]

Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates
2022 arXiv   pre-print
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems.  ...  Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation  ...  Recommendation systems with implicit feedback are more commonly seen in real-world application scenarios since implicit feedback is easier to collect, compared to explicit feedback [34] .  ... 
arXiv:2208.01709v2 fatcat:k6ykqdqpxje55haupziatr7gl4

Task-adaptive Neural Process for User Cold-Start Recommendation [article]

Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang
2021 arXiv   pre-print
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited.  ...  TaNP is a new member of the neural process family, where making recommendations for each user is associated with a corresponding stochastic process.  ...  MetaLWA and MetaNLBA are only applicable to implicit feedback datasets, so the results of them on MovieLens-1M are not provided.  ... 
arXiv:2103.06137v1 fatcat:cxkie6g4lfattkmrjf2ajjau3e

Location based learning of user behavior for proactive recommender systems in car comfort functions

Thomas Stone, Olga Birth, Andre Gensler, Andreas Huber, Martin Jänicke, Bernhard Sick
2014 Jahrestagung der Gesellschaft für Informatik  
Automating such functions as proactive recommender systems would exploit the full potential for decreasing driver stress.  ...  The model applies second-order uncertainty to evaluate the certainty about inferred parameter values and it deals with novelty and decaying observations explicitly.  ...  Outlook In future work we will enhance the presented model's targeted set of functions.  ... 
dblp:conf/gi/StoneBGHJS14 fatcat:xvimdgctzbdsrivp7ly77beihq
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