A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

2020 ◽  
Author(s):  
Hongwei Wang ◽  
Shiqin Chen
Keyword(s):  
Collaborative Filtering ◽  
Bipartite Graph ◽  
Sparse Data ◽  
Experimental Results ◽  
Local Similarity ◽  
New Model ◽  
Global Similarity

It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

scholarly journals NSiteMatch: Prediction of Binding Sites of Nucleotides by Identifying the Structure Similarity of Local Surface Patches

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jie Sun ◽  
Ke Chen
Keyword(s):  
Binding Sites ◽  
Surface Patch ◽  
Energy Calculation ◽  
Local Similarity ◽  
Success Rates ◽  
Biologically Relevant ◽  
Surface Patches ◽  
Extensive Evaluation ◽  
Structure Similarity ◽  
Global Similarity

Nucleotides play a central role in life-form metabolism, by interacting with proteins and mediating the function of proteins. It is estimated that nucleotides constitute about 15% of the biologically relevant ligands included in PDB. Prediction of binding sites of nucleotides is useful in understanding the function of proteins and can facilitate the in silico design of drugs. In this study, we propose a nucleotide-binding site predictor, namely, NSiteMatch. The NSiteMatch algorithm integrates three different strategies: geometrical analysis, energy calculation, and template comparison. Unlike a traditional template-based predictor, which identifies global similarity between target structure and template, NSiteMatch concerns the local similarity between a surface patch of the target protein and the binding sites of template. To this end, NSiteMatch identifies more templates than traditional template-based predictors. The NSiteMatch predictor is compared with three representative methods, Findsite, Q-SiteFinder, and MetaPocket. An extensive evaluation demonstrates that NSiteMatch achieves higher success rates than Findsite, Q-SiteFinder, and MetaPocket, in prediction of binding sites of ATP, ADP, and AMP.

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

scholarly journals Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Collaborative Filtering ◽  
Processing Speed ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Computational Time ◽  
Data Sets ◽  
Heterogeneous Cluster ◽  
The Matrix

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.

scholarly journals Interesting Activities Discovery for Moving Objects Based on Collaborative Filtering

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guan Yuan ◽  
Shixiong Xia ◽  
Yanmei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Moving Objects ◽  
Experimental Results ◽  
Location Based Service ◽  
Trajectory Data ◽  
Hybrid Collaborative Filtering

With the development of location-based service, more and more moving objects can be traced, and a great deal of trajectory data can be collected. Finding and studying the interesting activities of moving objects from these data can help to learn their behavior very well. Therefore, a method of interesting activities discovery based on collaborative filtering is proposed in this paper. First, the interesting degree of the objects' activities is calculated comprehensively. Then, combined with the newly proposed hybrid collaborative filtering, similar objects can be computed and all kinds of interesting activities can be discovered. Finally, potential activities are recommended according to their similar objects. The experimental results show that the method is effective and efficient in finding objects' interesting activities.

New model of dielectric strength of Debye relaxation in monohydroxy alcohols

2019 ◽  
Vol 33 (26) ◽  
pp. 1950313
Author(s):  
Li-Na Wang ◽  
Xing-Yu Zhao ◽  
Yi-Neng Huang
Keyword(s):  
Experimental Data ◽  
Exact Solution ◽  
High Temperature ◽  
Ising Model ◽  
Dielectric Spectroscopy ◽  
Dielectric Strength ◽  
Experimental Results ◽  
Debye Relaxation ◽  
New Model ◽  
Model Predictions

The Debye relaxation of dielectric spectroscopy exists extensively in monohydroxy alcohols, and the existing theory of the dielectric strength is obviously inconsistent with the experimental results. In this paper, we propose an Ising model of infinite free-rotating pseudospin chains and get the exact solution of the dielectric strength versus temperature. The model predictions are qualitatively consistent with the experimental results, especially the crossover from the low to the high-temperature Curie–Weiss law. The quantitative comparisons indicate that the model predictions can agree well with the experimental data below 250 K.

Speech Enhancement Using Heterogeneous Information

2018 ◽  
Vol 10 (3) ◽  
pp. 46-59
Author(s):  
Yan Xiong ◽  
Fang Xu ◽  
Qiang Chen ◽  
Jun Zhang
Keyword(s):  
Speech Enhancement ◽  
Experimental Results ◽  
Noisy Environments ◽  
Heterogeneous Information ◽  
New Model ◽  
Model Based ◽  
Single Stream

This article describes how to use heterogeneous information in speech enhancement. In most of the current speech enhancement systems, clean speeches are recovered only from the signals collected by acoustic microphones, which will be greatly affected by the acoustic noises. However, heterogeneous information from different kinds of sensors, which is usually called the “multi-stream,” are seldom used in speech enhancement because the speech waveforms cannot be recovered from the signals provided by many kinds of sensors. In this article, the authors propose a new model-based multi-stream speech enhancement framework that can make use of the heterogeneous information provided by the signals from different kinds of sensors even when some of them are not directly related to the speech waveform. Then a new speech enhancement scheme using the acoustic and throat microphone recordings is also proposed based on the new speech enhancement framework. Experimental results show that the proposed scheme outperforms several single-stream speech enhancement methods in different noisy environments.

A Network-Based Multi-Dimensional Recommendation Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 1218-1222
Author(s):  
Xiang Yun Xiong ◽  
Yu Chen Fu ◽  
Zhao Qing Liu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Better Than

Personalized recommendation based on bipartite network has attracted more and more attention. Its obviously better than CF (Collaborative Filtering). In this paper, we propose a multi-dimensional recommendation algorithm called BNPM. It combines item-based, user-based and category-based recommendation model to improve recommendation quality. The experimental results show that the algorithm can improve the diversity and reduce the popularity on the base of holding the accuracy of the recommendation

A harmonic drive model considering geometry and internal interaction

2016 ◽  
Vol 231 (4) ◽  
pp. 728-743 ◽  
Author(s):  
Chuang Zou ◽  
Tao Tao ◽  
Gedong Jiang ◽  
Xuesong Mei ◽  
Junhui Wu
Keyword(s):  
Ball Bearing ◽  
Experimental Results ◽  
Torsional Stiffness ◽  
Harmonic Drive ◽  
Low Velocity ◽  
New Model ◽  
Internal Interaction ◽  
Drive Systems ◽  
Assembly Error ◽  
Drive Model

A new harmonic drive model considering the geometry, internal interactions and assembly error of key parts is proposed in this paper. In this model, a single tooth pair is used to represent the transmission mechanism of harmonic drive. The meshing stiffness between the flexspline and the circular spline, the torsional stiffness of the flexspline cylinder, and the radial stiffness of the thin-walled ball bearing are included and formulated. The kinematic error is fitted using a low-velocity test, and its generating mechanism is analysed. The friction of the harmonic drive is formulated at the tooth meshing section and at the ball bearing, where its parameters are identified based on experimental results. Based on the new model, velocity step simulations are conducted. For comparison, velocity step experiments at eight different velocities from 60 to 3000 r/min are performed, and the simulation results are in good agreement with the experimental results. The new model reveals the dynamic behaviour of the harmonic drive system; therefore, it will be useful for the dynamic design and precision control of harmonic drive systems.

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