scholarly journals Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 205 ◽  
Author(s):  
Shanyun Liu ◽  
Yunquan Dong ◽  
Pingyi Fan ◽  
Rui She ◽  
Shuo Wan
Keyword(s):  
User Behavior ◽  
User Preference ◽  
Importance Measure ◽  
Data Sets ◽  
Target Revenue ◽  
Importance Coefficient ◽  
Expected Revenue ◽  
Utility Distribution ◽  
The One ◽  
Recommendation Strategy

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.

scholarly journals Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xuelian Yang ◽  
Jin Bai ◽  
Xiaolin Wang
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Labor Cost ◽  
Internet Technology ◽  
Social Model ◽  
User Preference ◽  
Data Sets ◽  
Marketing Model ◽  
Dynamic Attention ◽  
The Cost

With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value.

scholarly journals A Novel Hybrid Similarity Calculation Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoping Fan ◽  
Zhijie Chen ◽  
Liangkun Zhu ◽  
Zhifang Liao ◽  
Bencai Fu
Keyword(s):  
Nearest Neighbor ◽  
Calculation Model ◽  
User Preference ◽  
Data Sets ◽  
Random Forest Algorithm ◽  
User Interest ◽  
Function Fitting ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
The One

This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.

scholarly journals Analysis of Risk Factors in Dementia Through Machine Learning

2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Algorithms ◽  
Progressive Increase ◽  
Control Group ◽  
Data Sets ◽  
Modifiable Factors ◽  
Validation Set ◽  
The One ◽  
And Control ◽  
Potential Tool

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

scholarly journals Non-Compensatory Psychological Models for Recommender Systems

2019 ◽  
Vol 33 ◽  
pp. 4304-4311 ◽  
Author(s):  
Chen Lin ◽  
Xiaolin Shen ◽  
Si Chen ◽  
Muhua Zhu ◽  
Yanghua Xiao
Keyword(s):  
Conceptual Model ◽  
Prediction Models ◽  
User Preference ◽  
Data Sets ◽  
Real World Data ◽  
Ranking Models ◽  
Psychological Models ◽  
Unexplored Area ◽  
Latent Factor Models ◽  
Consumption Decision

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models.Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.

Data Documents

2021 ◽  
Vol 48 (4) ◽  
pp. 307-328
Author(s):  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane ◽  
Birger Hjørland ◽  
◽  
...  
Keyword(s):  
Citation Index ◽  
The Other ◽  
Data Sets ◽  
Bibliometric Data ◽  
Model Data ◽  
Other Hand ◽  
Data Citation ◽  
Philosophical Assumption ◽  
The One

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as “a single, fixed truth, valid for everyone, everywhere, at all times”

Statistical Uncertainty in Long-Term Distributions of Significant Wave Height

1996 ◽  
Vol 118 (4) ◽  
pp. 284-291 ◽  
Author(s):  
C. Guedes Soares ◽  
A. C. Henriques
Keyword(s):  
Probability Distribution ◽  
Wave Height ◽  
Significant Wave Height ◽  
Data Sets ◽  
Monthly Basis ◽  
Significant Wave ◽  
Fitting Procedure ◽  
The One ◽  
Homogeneous Data

This work examines some aspects involved in the estimation of the parameters of the probability distribution of significant wave height, in particular the homogeneity of the data sets and the statistical methods of fitting a distribution to data. More homogeneous data sets are organized by collecting the data on a monthly basis and by separating the simple sea states from the combined ones. A three-parameter Weibull distribution is fitted to the data. The parameters of the fitted distribution are estimated by the methods of maximum likelihood, of regression, and of the moments. The uncertainty involved in estimating the probability distribution with the three methods is compared with the one that results from using more homogeneous data sets, and it is concluded that the uncertainty involved in the fitting procedure can be more significant unless the method of moments is not considered.

scholarly journals Asymptotic analysis for personalized Web search

2010 ◽  
Vol 42 (02) ◽  
pp. 577-604 ◽  
Author(s):  
Yana Volkovich ◽  
Nelly Litvak
Keyword(s):  
Web Search ◽  
Power Laws ◽  
User Preference ◽  
Importance Measure ◽  
Web Documents ◽  
Original Definition ◽  
Theoretical Predictions ◽  
Heavy Tailed ◽  
Definition Of ◽  
Good Agreement

PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equationwhere theRis are distributed asR. This equation is inspired by the original definition of the PageRank. In particular,Nmodels the number of incoming links to a page, andBstays for the user preference. Assuming thatNorBare heavy tailed, we employ the theory of regular variation to obtain the asymptotic behavior ofRunder quite general assumptions on the involved random variables. Our theoretical predictions show good agreement with experimental data.

scholarly journals The insoluble problems of books: what does Altmetric.com have to offer?

2018 ◽  
Vol 70 (6) ◽  
pp. 691-707 ◽  
Author(s):  
Daniel Torres-Salinas ◽  
Juan Gorraiz ◽  
Nicolas Robinson-Garcia
Keyword(s):  
Special Focus ◽  
Data Sets ◽  
Content Type ◽  
Social Sciences And Humanities ◽  
Promising Tool ◽  
The Social ◽  
Google Books ◽  
Data Source ◽  
Alternative Sources ◽  
The One

Purpose The purpose of this paper is to analyze the capabilities, functionalities and appropriateness of Altmetric.com as a data source for the bibliometric analysis of books in comparison to PlumX. Design/methodology/approach The authors perform an exploratory analysis on the metrics the Altmetric Explorer for Institutions, platform offers for books. The authors use two distinct data sets of books. On the one hand, the authors analyze the Book Collection included in Altmetric.com. On the other hand, the authors use Clarivate’s Master Book List, to analyze Altmetric.com’s capabilities to download and merge data with external databases. Finally, the authors compare the findings with those obtained in a previous study performed in PlumX. Findings Altmetric.com combines and orderly tracks a set of data sources combined by DOI identifiers to retrieve metadata from books, being Google Books its main provider. It also retrieves information from commercial publishers and from some Open Access initiatives, including those led by university libraries, such as Harvard Library. We find issues with linkages between records and mentions or ISBN discrepancies. Furthermore, the authors find that automatic bots affect greatly Wikipedia mentions to books. The comparison with PlumX suggests that none of these tools provide a complete picture of the social attention generated by books and are rather complementary than comparable tools. Practical implications This study targets different audience which can benefit from the findings. First, bibliometricians and researchers who seek for alternative sources to develop bibliometric analyses of books, with a special focus on the Social Sciences and Humanities fields. Second, librarians and research managers who are the main clients to which these tools are directed. Third, Altmetric.com itself as well as other altmetric providers who might get a better understanding of the limitations users encounter and improve this promising tool. Originality/value This is the first study to analyze Altmetric.com’s functionalities and capabilities for providing metric data for books and to compare results from this platform, with those obtained via PlumX.

The Study and Design of a UDP Flow Measurement System

2013 ◽  
Vol 278-280 ◽  
pp. 946-949
Author(s):  
Hai Feng Guo
Keyword(s):  
Complex Network ◽  
Measurement System ◽  
Flow Measurement ◽  
Data Flow ◽  
Comparison Function ◽  
Data Sets ◽  
Network Environment ◽  
Network Bandwidth ◽  
Packet Capture ◽  
The One

Proposed a way to UPD flow and UPD system ideology. The system is considered the one-way characteristics of UDP flow in the backbone of the network, used the WinPcap packet capture technology. The system including network packet captures module, packet replay module, packets spell flow module, UDP analysis module, while using the map template classes in stl, improved the performance of UDP packets through a comparison function with efficient custom;Contrast to the data characteristics under the complex network environment, the system adopts the step-by-step small tools design way to facilitate the system to expand new analysis function. Through the three sets of data : a backbone data sets and two DARPA1999 data sets, it can be seen that the overall development of UDP data flow is expanding the network bandwidth , and small UDP flows is more.The quicker network bandwidth development, the shorter the UDP flows average time.

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