scholarly journals Multi-Component Graph Convolutional Collaborative Filtering

2020 ◽  
Vol 34 (04) ◽  
pp. 6267-6274
Author(s):  
Xiao Wang ◽  
Ruijia Wang ◽  
Chuan Shi ◽  
Guojie Song ◽  
Qingyong Li
Keyword(s):  
Collaborative Filtering ◽  
Research Effort ◽  
User Preference ◽  
Cost Performance ◽  
Fine Grained ◽  
Multiple Components ◽  
Significant Performance ◽  
Performance Gains ◽  
Latent Components ◽  
Component Graph

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.

scholarly journals IMPROVING PERSONALIZED QUESTIONNAIRE WITH REDUNDANCY REDUCTION FOR ADDRESSING COLD USER PROBLEM

2021 ◽  
Vol 5 (1) ◽  
pp. 457-466
Author(s):  
Umar Kabiru ◽  
Abubakar Muhammad
Keyword(s):  
Collaborative Filtering ◽  
Performance Improvement ◽  
Online Shopping ◽  
Cold Start ◽  
Reduction Technique ◽  
User Preference ◽  
Redundancy Reduction ◽  
Significant Performance ◽  
Filtering Techniques

User-based and item-based collaborative filtering techniques are among most explored strategies of making products’ recommendations to Users on online shopping platforms. However, a notable weakness of the collaborative filtering techniques is the cold start problem. Which include cold user problem, cold item problem and cold system problem – i.e., the failure of collaborative filtering to make recommendation of products to a new user, failure of an item to be recommended, or combination of the two respectively.  Literature investigation has shown that cold user problem could be effectively addressed using technique of personalized questionnaire. Unfortunately, where the products’ database is too large (as in Amazon.com), results obtained from personalized questionnaire technique could contain some user preference uncertainties. This paper presents technique of improving personalized questionnaire with uncertainty reduction technique. In addition, the paper presents classification of product recommendation systems. In this work we will be limited to user-based cold start.  Experimentation was conducted using Movielens dataset, where the proposed technique achieved significant performance improvement over personalized questionnaire technique with RMSE, Precision, Recall,1 and NDCG of 0.200, 0.227, 0.261, 0.174 and 0.249

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

scholarly journals DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Deepta Rajan ◽  
Sameeksha Katoch ◽  
Andreas Spanias
Keyword(s):  
Electronic Health Records ◽  
Rapid Development ◽  
Benchmark Problems ◽  
Health Records ◽  
Automatic Interpretation ◽  
Deep Architecture ◽  
Significant Performance ◽  
Electronic Health ◽  
Improving Accuracy ◽  
Performance Gains

Abstract Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

Simulation of a Floating Offshore Wind Turbine With an Integrated Response Mitigation Technology

2018 ◽  
Author(s):  
Christopher K. Allen ◽  
Andrew J. Goupee ◽  
Jeffrey Lindner ◽  
Robert Berry
Keyword(s):  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
The Novel ◽  
Mass Reduction ◽  
Turbine Performance ◽  
Additional Equipment ◽  
Significant Performance ◽  
Integrated Response ◽  
The University ◽  
Performance Gains

This work investigates the implementation of a novel, NASA-developed Fluid Harmonic Absorber (FHA) technology to mitigate platform motions and structural loads that can lead to lighter platforms, increased turbine performance, and ultimately, a lower LCOE. The novel damping strategy takes advantage of existing water ballast in the VolturnUS semi-submersible platform to achieve significant performance gains with minimal additional equipment and complexity. NREL’s FOWT software FAST is modified to include the primary features of the FHA technology. A study of the University of Maine-developed VolturnUS semi-submersible FOWT augmented with FHA technology is undertaken to quantify global performance of the system. When compared to the baseline technology, numerical simulations of a redesigned platform utilizing the FHA dampers indicate a reduction of 15.8% in hull structural material. Finally, the improvements in LCOE resulting from this mass reduction are assessed to demonstrate the advantages of NASA’s FHA technology for FOWT applications.

scholarly journals A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 648 ◽  
Author(s):  
Xiangpeng Wan ◽  
Hakim Ghazzai ◽  
Yehia Massoud
Keyword(s):  
Waiting Time ◽  
Large Scale ◽  
Recommendation System ◽  
Performance Metrics ◽  
Routing Algorithm ◽  
Area Of Interest ◽  
The Future ◽  
Taxi Service ◽  
Significant Performance ◽  
Performance Gains

Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.

scholarly journals Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts

2019 ◽  
Vol 33 ◽  
pp. 7884-7891 ◽  
Author(s):  
Ximing Li ◽  
Jiaojiao Zhang ◽  
Jihong Ouyang
Keyword(s):  
Topic Model ◽  
Topic Models ◽  
Manifold Regularization ◽  
Neighborhood Structure ◽  
Short Text ◽  
Semantic Level ◽  
Clustering And Classification ◽  
Significant Performance ◽  
Sparsity Problem ◽  
Performance Gains

Conventional topic models suffer from a severe sparsity problem when facing extremely short texts such as social media posts. The family of Dirichlet multinomial mixture (DMM) can handle the sparsity problem, however, they are still very sensitive to ordinary and noisy words, resulting in inaccurate topic representations at the document level. In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations. This is achieved by using variational manifold regularization, constraining the close short texts should have similar variational topic representations. Upon this idea, we propose a novel Laplacian DMM (LapDMM) topic model. During the document graph construction, we further use the word mover’s distance with word embeddings to measure document similarities at the semantic level. To evaluate LapDMM, we compare it against the state-of-theart short text topic models on several traditional tasks. Experimental results demonstrate that our LapDMM achieves very significant performance gains over baseline models, e.g., achieving even about 0.2 higher scores on clustering and classification tasks in many cases.

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