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

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.

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Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Scientific Programming ◽

10.1155/2021/2592604 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Tongyan Li ◽

Yingxiang Li ◽

Chen Yi-Ping Phoebe

Keyword(s):

Collaborative Filtering ◽

Sparse Data ◽

Current Data ◽

Prediction Algorithm ◽

Filtering Algorithm ◽

Recommendation Algorithms ◽

Collaborative Filtering Algorithm ◽

Scoring Matrix ◽

Rating Prediction ◽

User Ratings

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

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A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.747 ◽

2014 ◽

Vol 610 ◽

pp. 747-751

Author(s):

Jian Sun ◽

Xiao Ying Chen

Keyword(s):

Collaborative Filtering ◽

Prediction Accuracy ◽

Cloud Model ◽

Prediction Algorithm ◽

Recommendation Algorithm ◽

Rating Data ◽

User Data ◽

Item Attribute ◽

Rating Prediction ◽

User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

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Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120556 ◽

2019 ◽

Vol 8 (12) ◽

pp. 556

Author(s):

Wen Chen ◽

Zhiyun Xu ◽

Xiaoyao Zheng ◽

Yonglong Luo

Keyword(s):

Time Series ◽

Data Processing ◽

Data Preprocessing ◽

Research Data ◽

Processing Methods ◽

Flow Prediction ◽

Tourism Studies ◽

Tourist Flows ◽

Data Processing Methods ◽

Tourist Flow

Technological advances have led to numerous developments in data sources. Geo-tagged photo metadata has provided a new source of mass research data for tourism studies. A series of data processing methods centering on the various types of information contained in geo-tagged photo metadata have thus been proposed; as a result, the development of tourism studies based on such data has advanced. However, an in-depth study of the data processing methods designed to conduct tourist flow prediction based on geo-tagged photo metadata has not yet been conducted. In order to acquire accurate substitutive data regarding inbound flows in cities, this paper introduces and designs several methods, including data screening, text data similarity calculation, geographical location clustering, and time series data modelling, in order to realize a data preprocessing model for inbound tourist flows in cities based on geo-tagged photo metadata. Wherein, the entropy filtering method was introduced to aid in determining whether the data were posted by inbound tourists; whether the inbound persons’ activities were related to tourism was judged through the calculation of tag text similarity; an efficient clustering method based on geographic grid partition was designed for cases in which the tag values were empty; finally, the time series of the inbound tourist flows of a certain region and period were obtained through data statistics and normalization. For the empirical research, Beijing City in China was selected as the research case, after which the feasibility and accuracy of the methods proposed in this paper were verified through data correlation analysis between Flickr data and real statistical yearbook data, as well as analysis of the prediction results based on a machine learning algorithm. The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction. These methods can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.

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A Movie Rating Prediction Algorithm with Collaborative Filtering

2010 International Conference on Advances in Social Networks Analysis and Mining ◽

10.1109/asonam.2010.64 ◽

2010 ◽

Cited By ~ 9

Author(s):

O. Bora Fikir ◽

Ilker O. Yaz ◽

Tansel Özyer

Keyword(s):

Collaborative Filtering ◽

Prediction Algorithm ◽

Rating Prediction

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ANALYSIS OF THE FLOODING SYSTEM WITH APPLICATION OF STATISTICAL DATA PROCESSING METHODS

Oilfield Engineering ◽

10.30713/0207-2351-2020-8(620)-5-9 ◽

2020 ◽

pp. 5-9

Author(s):

A.Yu. Sentsov ◽

◽

I.V. Ryabov ◽

A.A. Ankudinov ◽

Yu.E. Radevich ◽

...

Keyword(s):

Data Processing ◽

Statistical Data ◽

Processing Methods ◽

Statistical Data Processing ◽

Data Processing Methods

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The experimental data processing methods as a consequence of agricultural experiments

British journal of innovation in science and technology ◽

10.22406/bjist-17-2.5-29-39 ◽

2017 ◽

pp. 29-39 ◽

Cited By ~ 1

Author(s):

S. V. Belousov ◽

A. I. Belousova

Keyword(s):

Experimental Data ◽

Data Processing ◽

Processing Methods ◽

Experimental Data Processing ◽

Data Processing Methods

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Use of Mathematical Modeling in Building Ceramics Functional Properties Studies

Materials Science Forum ◽

10.4028/www.scientific.net/msf.870.191 ◽

2016 ◽

Vol 870 ◽

pp. 191-195

Author(s):

N.A. Vil'bitskaya ◽

S.A. Vilbitsky ◽

A.G. Avakyan

Keyword(s):

Mathematical Modeling ◽

Data Processing ◽

Ceramic Material ◽

Functional Properties ◽

Statistical Data ◽

Ceramic Tiles ◽

Processing Methods ◽

Statistical Data Processing ◽

High Calcium ◽

Data Processing Methods

The peculiarities of using mathematical and statistical data processing methods in studying the intensification in the process of sintering a ceramic material with a high content of high-calcium waste, and mineralizing sintering lithium-containing waste were studied. The region of optimal ceramic masses composition, which allows obtaining ceramic tiles with high functional properties, was defined.

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