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User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning [article]

Menghan Wang, Xiaolin Zheng, Kun Zhang
2018 arXiv   pre-print
We first cluster users based on the recommendation predictions, then we use multi-task learning to learn the user-sensitive ensemble function for the users.  ...  This paper considers recommendation algorithm ensembles in a user-sensitive manner.  ...  Ensemble Learning with Grouped Users For each user we learn a linear function f i (X K i ) = w T i X K i to combine the results.  ... 
arXiv:1804.10795v1 fatcat:e3xsf6gcrretnhx3n67gnr5s5i

Sentiment Analysis using Ensemble Classifier on Real Time Data Set

Aman Goenka
2020 International Journal for Research in Applied Science and Engineering Technology  
The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, microblogs, Twitter, and social networks.  ...  It is difficult to analyze or summarize the user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc.  ...  Rodriguez and Clark proposed the use of classifier ensembles at expression-level, which is related to Contextual Polarity Disambiguation.  ... 
doi:10.22214/ijraset.2020.2052 fatcat:anu54qc6unbefowcqvhfc2aaf4

Improving Business Pages Recommendation in Social Network Using Link Prediction Methods

Watare Asaph, Shaowei Sun
2021 Asian Journal of Probability and Statistics  
More specifically, given a company in thenetwork, we want to recommend potential companies to connect with.  ...  Link prediction is a common problem that has broad application in such social networks, ranging from predicting unobserved interaction to recommending related items.  ...  Acknowledgement The acknowledgements to people who provided assistance.  ... 
doi:10.9734/ajpas/2021/v14i230322 fatcat:73n72s3exrg4vj4ymqo357lmpy

Review of Social Collaborative Filtering Recommender System's Methods

Pratibha Yadav
2016 International Journal Of Engineering And Computer Science  
Recommender Systems plays a vital role in e-commerce. The goal of recommender system is to present the user with the personalized information that matches with the user's interest.  ...  Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user.  ...  Circle based recommendation Circle-based Recommendation models are an extension of the SMF model to social networks with inferred friend circles [21] .  ... 
doi:10.18535/ijecs/v4i10.49 fatcat:roo2xsud3fhmrhca7wjeawcy7y

Review of Social Collaborative Filtering Recommender System's Methods

Pratibha Yadav
2016 International Journal Of Engineering And Computer Science  
Recommender Systems plays a vital role in e-commerce. The goal of recommender system is to present the user with the personalized information that matches with the user's interest.  ...  Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user.  ...  Circle based recommendation Circle-based Recommendation models are an extension of the SMF model to social networks with inferred friend circles [21] .  ... 
doi:10.18535/ijecs/v4i11.06 fatcat:4id6npgcvfdhfo4i7if4y7k56i

Styling with Attention to Details [article]

Ayushi Dalmia, Sachindra Joshi, Raghavendra Singh, Vikas Raykar
2018 arXiv   pre-print
We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to complete the fashion ensemble.  ...  Complementary items are those items that can be worn along with the selected items according to the style.  ...  RELATED WORK This work is closely related to two sub-fields: application of recommender systems and deep learning.  ... 
arXiv:1807.01182v1 fatcat:wy5ilagurbgqfnjkoqbso6qucq

Concept Drift Awareness in Twitter Streams

Joana Costa, Catarina Silva, Mario Antunes, Bernardete Ribeiro
2014 2014 13th International Conference on Machine Learning and Applications  
In this paper we present a learning strategy to learn with drift in the occurrence of concepts in Twitter.  ...  The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones.  ...  Users link themselves by defining others to follow, and consequently have their own followers based not only on social relations but also related with topics of interest.  ... 
doi:10.1109/icmla.2014.53 dblp:conf/icmla/CostaSAR14 fatcat:ixfvx2iaqbcm7je4xj32x327qm

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama, Amir H. Gandomi
2020 Zenodo  
The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models.  ...  Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey.  ...  Ensemble machine learning algorithms Ensemble machine learning algorithms or ensemble learning (EL) models use multiple learning algorithms to improve training processes and boost learning from data [  ... 
doi:10.5281/zenodo.4087812 fatcat:4flgeabkxvgjrpbydfby3v6tua

Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens [article]

Saman Forouzandeh, Mehrdad Rostami, Kamal Berahmand
2020 arXiv   pre-print
In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with  ...  In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems.  ...  in social networks [14] , and recommendation of various items to users.  ... 
arXiv:2008.01192v1 fatcat:qdzafgw3yngwlna5by6xu34bmq

Network Embedding For Link Prediction in Bipartite Networks

Özge KART
2021 European Journal of Science and Technology  
Ensemble learning algorithms have been applied for supervised link prediction.  ...  The experiments performed on two bipartite social networks built from public datasets led promising results with 0.939 and 0.974 AUC values.  ...  For example, social networkbased recommender systems need to capture the relations between users and items that the users rate.  ... 
doi:10.31590/ejosat.937722 fatcat:fgl3ran6lzdfxps3guzuqddu2i

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning [chapter]

Aliaksandr Barushka, Petr Hajek
2018 IFIP Advances in Information and Communication Technology  
To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners.  ...  Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem.  ...  However, surprisingly little attention has been paid to neural networks with ensemble learning.  ... 
doi:10.1007/978-3-319-92007-8_4 fatcat:7ah45uzgabebrn4whha63lijkq

Recommending Insurance products by using Users' Sentiments [article]

Rohan Parasrampuria, Ayan Ghosh, Suchandra Dutta, Dhrubasish Sarkar
2021 arXiv   pre-print
of sentiment analysis with a recommendation.  ...  Then we matched the polarity score along with the user's profiles and generated the list of insurance products to be recommended in descending order.  ...  It's a machine-learning algorithm that relies on ensemble learning for classification. It is a method of ensemble learning.  ... 
arXiv:2108.06210v1 fatcat:xvzcvhkysbbfrl5z6j6cbuqjr4

Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective

Honghao Gao, Xi Qin, Ramon J. Duran Barroso, Walayat Hussain, Yueshen Xu, Yuyu Yin
2020 IEEE Transactions on Emerging Topics in Computational Intelligence  
Current related recommendation methods have defects.  ...  We enhance the matrix factorization (MF) model with the mined implicit knowledge that are implicit relationships on both sides. We build an ensemble model by using all implicit knowledge.  ...  The service social network was constructed based on the information related to the mashup service, Web API, and API tags.  ... 
doi:10.1109/tetci.2020.3023155 fatcat:6k6rnxcx2bdmrdv6nowguer42m

Sentiment Analysis for Product Recommendation Using Random Forest

Gayatri Khanvilkar, Prof. Deepali Vora
2018 International Journal of Engineering & Technology  
The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.  ...  Major product websites are using sentiment analysis to understand the popularity and problems with the product.  ...  There are different inferences related to sentiment analysis are mention in table 1. A recommendation system identifies and provides recommended content or digital items for users.  ... 
doi:10.14419/ijet.v7i3.3.14492 fatcat:xn26yp2375ektbzjqi4ik6idvm

Social recommender approach for technology-enhanced learning

Mohammed Tadlaoui, Karim Sehaba, Sébastien George, Azeddine Chikh, Karim Bouamrane
2018 International Journal of Learning Technology  
The first two hypotheses are related to the approach, the third is related to the Icraa learning environment and the last one is related to both of them.  ...  The ensemble recommender system Ensemble uses the two existing forms of social navigation, namely collaborative filtering and history-enriched information spaces to guide users.  ... 
doi:10.1504/ijlt.2018.091631 fatcat:43hnr2lwpfac5l4fkwzcb63o6m
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