Personalized Emotional Prediction Method for Real-Life Objects Based on Collaborative Filtering

2011 ◽  
pp. 45-52
Author(s):  
Hyeong-Joon Kwon ◽  
Hyeong-Oh Kwon ◽  
Kwang-Seok Hong
Keyword(s):  
Collaborative Filtering ◽  
Real Life ◽  
Prediction Method

scholarly journals Epileptic Seizure Prediction over EEG Data using Hybrid CNN-SVM Model with Edge Computing Services

2018 ◽  
Vol 210 ◽  
pp. 03016 ◽  
Author(s):  
Punjal Agarwal ◽  
Hwang-Cheng Wang ◽  
Kathiravan Srinivasan
Keyword(s):  
Epileptic Seizure ◽  
Life Support ◽  
Real Life ◽  
Prediction Method ◽  
Edge Computing ◽  
Support Vector ◽  
Seizure Prediction ◽  
Computing Services ◽  
Svm Model ◽  
Epileptic Seizure Prediction

Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.

Association Rule Mining in Collaborative Filtering

2017 ◽  
pp. 159-179 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Fan Jiang ◽  
Edson M. Dela Cruz ◽  
Vijay Sekar Elango
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
Association Rules ◽  
Data Structures ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Life ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Association Rule Miner

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

scholarly journals Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering

2019 ◽  
Vol 6 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Jianrui Chen ◽  
Chunxia Zhao ◽  
Uliji ◽  
Lifang Chen
Keyword(s):  
Dimension Reduction ◽  
Collaborative Filtering ◽  
Data Clustering ◽  
Real Life ◽  
Nearest Neighbors ◽  
User Group ◽  
Recommendation Algorithm ◽  
Evolutionary Clustering ◽  
The Relationship ◽  
Score Matrix

Abstract In recent years, application of recommendation algorithm in real life such as Amazon, Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such as sparse data and low recommended accuracy. Collaborative filtering is a mature algorithm in the recommended systems, but there are still some problems. In this paper, a novel collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering is presented. Firstly, score matrix is pre-processed with normalization and dimension reduction, to obtain denser score data. Based on these processed data, clustering principle is generated and dynamic evolutionary clustering is implemented. Secondly, the search for the nearest neighbors with highest similar interest is considered. A measurement about the relationship between users is proposed, called user correlation, which applies the satisfaction of users and the potential information. In each user group, user correlation is applied to choose the nearest neighbors to predict ratings. The proposed method is evaluated using the Movielens dataset. Diversity experimental results demonstrate that the proposed method has outstanding performance in predicted accuracy and recommended precision.

scholarly journals Sustainable City: Energy Usage Prediction Method for Electrified Refuse Collection Vehicles

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 1100-1116
Author(s):  
Rui Zhao ◽  
Tudor Stincescu ◽  
Erica E. F. Ballantyne ◽  
David A. Stone
Keyword(s):  
Target Location ◽  
Real Life ◽  
Prediction Method ◽  
Energy Prediction ◽  
Refuse Collection ◽  
Real Life Data ◽  
Battery Capacity ◽  
City Space ◽  
Economic Strategies ◽  
Prediction Approach

With the initiative of sustainable smart city space, services and structures (3S), progress towards zero-emission municipal services has advanced the deployment of electric refuse collection vehicles (eRCVs). However, eRCVs are commonly equipped with oversized batteries which not only contribute to the majority of the weight of the vehicles but also remain a consistent weight, independent of the stage of charge (SoC), thus crucially jeopardising the significance of eRCVs in sustainability and economic strategies. Hence, customising the battery capacity in such a way that minimises its weight while storing ample energy for stalwart serviceability could significantly enhance its sustainability. In this study, taking only addresses as input, through an emergent two-stage data analysis, the energy required to collect refuse from a group of addresses was predicted. Therefore, predictions of the battery capacity requirement for the target location are possible. The theories and techniques presented in this paper were evaluated using real-life data from eRCV trials. For the same group of addresses, predicted results show an averaged error rate of 8.44%, which successfully demonstrates that using the proposed address-driven energy prediction approach, the energy required to collect refuse from a set of addresses can be predicted, which can provide a means to optimise the vehicle’s battery requirement.

A Novel Collaborative Filtering Recommendation Approach Based on Field Authorities

2013 ◽  
Vol 765-767 ◽  
pp. 989-993
Author(s):  
Zhi Xue ◽  
Yao Xue Zhang ◽  
Yue Zhi Zhou ◽  
Wei Hu
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Word Of Mouth ◽  
Real Life ◽  
Recommendation Algorithm ◽  
Computational Overhead ◽  
External Data ◽  
Data Limitations ◽  
Accuracy Level ◽  
Filtering Approach

This paper presents a novel collaborative filtering recommendation algorithm based on field authorities which simulates the real life word of mouth recommendation mode. It uses the specialistic knowledge from field authorities of different genres, and successfully addresses data sparsity and noise problems existing in traditional collaborative filtering. Meanwhile it also improves prediction accuracy and saves computational overhead effectively. Experiments on MovieLens datasets show that the accuracy of our algorithm is significantly higher than collaborative filtering approach based on experts, and has larger scope because of no external data limitations. Meanwhile, compared to traditional k-NN collaborative filtering, our algorithm has a better performance both in MAE and precision experiments, and the computational overhead has a decrease of 19.2% while they provide the same accuracy level.

EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING

2007 ◽  
Vol 21 (02) ◽  
pp. 311-331 ◽  
Author(s):  
NIKOS MANOUSELIS ◽  
CONSTANTINA COSTOPOULOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Experimental Analysis ◽  
Real Life ◽  
Multiattribute Utility ◽  
Numerous Application ◽  
Data Set ◽  
Recommendation Algorithms ◽  
Actual Operation ◽  
Design Options

Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment.

scholarly journals MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 242
Author(s):  
Jianlong Xu ◽  
Zicong Zhuang ◽  
Zhiyu Xia ◽  
Yuhui Li
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Prediction Method ◽  
Third Party ◽  
Superior Performance ◽  
Prediction Algorithm ◽  
Service Reliability ◽  
Vast Number ◽  
Personalized Services ◽  
Service Oriented

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.

scholarly journals Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

2022 ◽  
Vol 24 (1) ◽  
pp. 139-140
Author(s):  
Dr.S. Dhanabal ◽  
◽  
Dr.K. Baskar ◽  
R. Premkumar ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Real Life ◽  
Similarity Matrix ◽  
Recommendation Algorithm ◽  
Simulation Experiments ◽  
Average Similarity ◽  
Recommendation Accuracy ◽  
Item Item

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

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