User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

Prediction of users online purchase behavior based on selective ensemble learning

A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Nonfarm employment, agricultural machinery service purchase and agricultural production efficiency:An empirical study based on China's major wheat-producing areas

Based on the cross-sectional data of the survey conducted in China's main wheat producing areas at the end of 2019,this paper uses the translog production function model to estimate agricultural technical efficiency,and studies the relationship between nonfarm employment,agricultural machinery service purchase and agricultural production efficiency.The results show that the household non-agricultural employment ratio and non-agricultural income have a significant positive impact on the purchase of agricultural machinery services.In addition to the effect of scale efficiency,non-agricultural employment does not have a significant impact on agricultural technical efficiency,and labor transfer does not have a significant negative impact on agricultural production.

The Influence of Perceived Risk on Purchase Intention——A Case study of Taobao Online Shopping of Fresh Fruit

Based on perceived risk,the impacts of different kinds of risks on consumer's purchase intention are tested by using the investigation data of 2016,and perceived risks of consumers from different groups have different impacts on purchase intention. Via interviews and online questionnaires,425 copies of effective samples were obtained. The structural equation modeling is used for research. Results display that product risk significantly affects purchase intention,and perceived risk among different groups has significant difference. According to research conclusion,the authors think that consumer should actively collect all kinds of information and take effective risk avoidance measures; Taobao platform should continuously perfect purchase rule,punish the behavior of leaking buyers' information,and continuously update Alipay software to prevent hacker intrusion; business should guarantee product quality,open information and make scientific marketing strategy.

Influence Mechanism of Clothing Anchor Features on Consumers'Purchase Intention

Relying on the field of e-commerce live broadcast,the features of clothing anchors are divided into four dimensions:charm features,recommendation features,display features and interaction features.The intermediate variable of social presence is introduced to construct a theoretical model of e-commerce live broadcast consumers'purchase intention.Based on the questionnaire survey data collection,using SPSS 23.0 software and Amos 23.0 software to analyze the data,it is concluded that the features of charm,recommendation,display and interaction of the clothing anchors have a significant positive impact on consumers'purchase intentions.Social presence plays a mediating role in the influence of the features of charm,recommendation,display and interaction on consumer'purchase intention.

The Impacts of Agricultural Machinery Purchase Subsidies on Mechanized Crop Residue Recycling

Crop residue recycling can improve the quality of the cropland,and it has multiple economic and ecological benefits.However,such practice is with low adoption due to different constraints.In this paper,we use the survey data from Baoding,Hebei province,and use the probit model to explore how the agricultural machinery purchase subsidies affect the mechanized crop residue recycling.The results showed that several factors that affect farmers in adopting the practice of mechanized crop residue crop recycling.Among these factors,the cost of adopting such practice is significant.The agricultural machinery purchase subsidies can effectively reduce the cost of such practice,as well as promote mechanized crop residue recycling.The paper also proposed several actions in the future.They include increasing the subsidies on agricultural machinery purchase and increasing farmers' awareness on crop residue recycling.

Purchase willingness of new energy vehicles: A case study in Jinan City of China

Speeding up the promotion of new energy vehicles is an important measure to optimize the energy structure,promote energy conservation and emission reduction,and develop the economy sustainability.The research uses a questionnaire survey to analyze the residents’willingness to purchase new energy vehicles in Jinan Gty of China,and utilizes the binomial logistic regression model and Global Moran's I to explain the impact of three factors(including responden ts'personal characteristics and subjective cognition,products,and social environment)on the purchase willingness of new energy vehicles.According to the survey,75.12%of the responden ts consider buying new energy vehicles in the future,but only 11.66%of the respondents know new energy vehicles well.It can be seen that the respondents in Jinan City generally have an insufficient understanding of new energy vehicles.It may lead to a decline in residents’trust in new energy vehicles,which will in turm affect their purchase willingness.Based on the survey,we find that women who live far from the city center enjoy high incomes and have a low-carbon awareness,generally exhibit a higher willingness to purchase new energy vehicles.Spatial distribution of the purchase willingness has certain aggregation characteristics,showing a positive spatial correlation pattern.Purchase willingness has a certain positive diffusion effect in space,and areas with a higher purchase willingness have a positive driving effect on their surrounding regions.Spatial distribution of the purchase willingness can be used as one of the breakthroughs in promoting new energy vehicles.In addition,safety,price,after-sales service,and infrastruicture of new energy vehicles are important determinants of people's purchase willingness.Among the types of subsidies,financial subsidy is most effective on the residents’purchase wilingness.Our research provides an impor tant information for the promotion of new energy vehicles in the region.

Consumers'Purchase Intention of Geographical Indication Agricultural Products under the Background of Live-streaming Sales:An Exploratory Research Based on Grounded Theory

Exploring the mechanism for the formation of consumer purchase intentions of geographical indication agricultural products in the context of live-streaming sales can provide an important reference for brand marketing of geographical indication agricultural products.In this study,in-depth interviews were conducted with consumers of geographical indication agricultural products.Based on grounded theory,open coding,axial coding and selective coding were performed for interview text.Finally,21 concepts,7 subcategories and 3 main categories were obtained,and a model of the formation mechanism of the purchase intention of geographical indication agricultural products under the background of live-streaming sales was constructed,that is,"consumer cognition-consumer attitude-consumer behavior".Among them,consumer cognition includes two dimensions:the type of geographical indication agricultural products and the live-streaming appeal strategy,i.e.,the personal cognition of consumer and the promotion of live-streaming host's strategy.Consumer attitude is value perception of consumers,mainly including two dimensions of functional value and emotional value.Consumer behavior is the consumer's willingness to buy.It has been concluded that the types of geographical indication agricultural products interact with the live-streaming appeal strategies.Through the intermediary of consumers'value perception,consumers'purchase intention is generated.Among them,resource-oriented geographical indication agricultural products adopt rational live-streaming appeal strategies,which can enhance the consumer's perception of functional value,thereby promoting their purchase intention;and cultural and creative geographical indication agricultural products brands adopt perceptual live-streaming appeal strategies,which can enhance the emotional value perception of consumers,thereby promoting their purchase intention.

Antecedents of Microblogging Users’Purchase Intention Toward Celebrities’Merchandise:Perspectives of Virtual Community and Fan Economy

Over the last few decades,cross-fertilization between marketing and fandom studies in a mediated world has been rare,hindering the knowledge development for marketing practitioners in the Chinese fan economy context.The purpose of this study is to find and establish a conceptual framework that includes Online Interaction(OI),Parasocial Relationships(PSR)and virtual fan communities on Weibo,and how these contributory factors embedded in the interplay process of digital fandom practices in terms of enhancing fans’Purchase Intention(PI)and media consumption behaviours toward the celebrities’merchandise.Using an online survey instrument,this research collected 294 completed responses from fans who had online interactions with celebrities and engagement of virtual fan communities on Weibo.Key results and findings provided a clear framework of four antecedents based on the conceptual model and indicated that the high intensity of OI led to higher levels of the perception of PSR and the Sense of Virtual Community(SOVC).Increased PSR and the SOVC can be seen as significant positive predictors of the PI(a part of consumer identity construction as a fan)also.This study revealed the underlying mechanism of an emerging marketing genre,also provide useful implications of audiences’digital marketing practices for marketers,celebrities and policymakers.

Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

A Grey Decision Model Used in Bidding for Equipment Purchase

Bidding has long been used as a method for allocating and procuring goods andservices.An acurrate and comprehensive evaluation of bidders is the key to make the bid-ding a successful one for tenderee. Howoever, evaluation of bidders is a tough work and theresult of evaluation is always affected by the evaluator's understanding of the standardsset for evaluation, and the evaluator's expertise, experience and preferrence. We make aneffort to find a method for selecting bidders as accurately as possible. A grey decisionmodel is presented, and an example is illustrated to make the practioners kenow how to usethe model.

Willingness of returning migrant workers to purchase houses: A case study of 45 villages in Henan Province, China

The willingness of returning migrant workers to purchase houses has an important impact on local urbanization.At present,there are few researches on the willingness of returning migrant workers to purchase houses.The existing researches mostly focus on the perspective of sociology and economics,and few studies are conducted from the perspective of geography.Based on the survey data of 45 villages in Henan Province of China in 2019,this study used binary logistic method to study the influencing factors of the willingness of returning migrant workers to purchase houses.The willingness of returning migrant workers to purchase houses is affected by many factors,such as personal,family,village,and returning factors.Among them,age and returning period are negatively correlated with the willingness of returning migrant workers to purchase houses.Factors such as years of education,years of working,and per capita income of village are positively correlated with the willingness of returning migrant workers to purchase houses.Returning migrant workers in plains and hilly areas are more willing to purchase houses than those in mountainous areas.The formation of the willingness to purchase houses is mainly related to economic foundation,age,marital status,and other factors.Returning migrant workers are becoming more willing to purchase houses,and the expected locations are mainly concentrated in the county seats.In the context of China’s high density of rural population and rapid urbanization,more returning migrant workers are expected to purchase houses in cities and towns for the sake of employment,marriage,children’s education,and access to public services.As a result,there is an extensive market of potential purchasers,which affects the development of the local urban real estate industry and is of great significance for returning migrant workers to truly integrate into the cities.However,some returning migrant workers still choose to purchase houses in villages,mainly affected by traditional concepts,living habits,and the better development of villages.Therefore,it is of great practical significance to explore the willingness of returning migrant workers to purchase houses,so as to master the flow laws and future behavior of returning migrant workers to purchase houses.

Comparative Analysis of the Purchase Price of Raw Milk in the World

This paper selects 20 countries from the major dairy producing continents such as Oceania,the Americas,Europe and Asia,for the comparative analysis of the purchase price of raw milk in the world. Based on the summarization of general features of the world raw milk prices,this paper elaborates the fluctuations in the purchase price of raw milk in Oceania,the Americas,Europe and Asia,respectively,and carries out the comparative study of the gap between the domestic purchase price of raw milk and the world purchase price of raw milk.

Demand prediction and purchase optimization decision model for alloys in steel making

In this study,related models of alloy purchasing decision system in the Baoshan base of Baosteel are discussed.First,the corresponding relationship between steel grades and alloy consumption is established through metallurgical-mechanism modeling and statistical analysis.Then,the alloy-demand prediction model based on alloy unit consumption and time series analysis is developed by combining sales plans and historical data.Finally,the alloy purchasing and inventory optimization model is developed to minimize the total cost of purchase and storage by combining inventory optimization theories.

Construction of a Consumer Purchase Intention Model of Slimming Underwear

The main factors that affected consumer purchase intention toward slimming underwear(SU) were investigated through the construction of a consumer behavior model. The constructed model was tested by structural equation modeling. "Perceived risk" had a negative effect on the "attitude". "Perceived benefit" and "subjective norm" had positive impacts on the "attitude". And "attitude" significantly influenced consumer "purchase intention". However, "product knowledge" had no significant influence on the "purchase intention". The result provided initial framework for researchers to conduct further investigation and enlighten the SU enterprises to improve performance.

Inter-Purchase Time Prediction Based on Deep Learning

Inter-purchase time is a critical factor for predicting customer churn.Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points,operation issues,as well as customer expectations to proactively reduce reasons for churn.Although remarkable progress has been made,classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-,medium-,and long-term data are likely to interfere with each other sequentially.Different from literature,this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers.Moreover,the analysis of differences in the purchase behavior of customers has been particularly highlighted.The integrated self-organizing map and Recurrent Neural Network technique is proposed to not only address the problem of purchase behavior but also improve the prediction accuracy of inter-purchase time.The permutation importance method was used to identify crucial variables in the prediction model and to interpret customer purchase behavior.The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches on the transaction data provided by a leading e-retailer in Taiwan.This study provides a valuable reference for marketing professionals to better understand and develop strategies to attract customers to shorten their inter-purchase times.

Purchase Analysis Based on the Relationship Between Customers and Service Providers

Although the service industry takes up a large percentage in world economies and is situated in the center of all industries,it has been pointed out that its growth rate is smaller compared to the other industries,which is partly because of its characteristic features:“amorphousness”,“simultaneity”,and“heterogeneity”.Service providers are therefore required to be experienced and have a good business sense to succeed in this field.The aim of this research is to support those who are not experienced or do not have a good business sense,using scientific approach.This research tries to present recommendation,taking customers’value and compatibilities with employees into account,and is based on the assumption that customers and employees have one-to-one contact over a period.A total of 3,447 customers and 133 employees were classified according to their philosophies,needs,and abilities.For each case,customers’purchase histories are first interrelated with the result of questionnaire,and put purchase behavior into marketing using Bayesian network.Then the HUB was examined and extracted features of the data of the questionnaire,and executed the stochastic inference to present the recommendation.This procedure enabled us to extract the features of customers’purchase behavior,and it is turned out that compatibilities of customers and employees are more important than the difference of their values and abilities.

Power Purchase Model of Provincial Grids Based on Diverse Energy Coordination Hedging Optimization

Under the background of energy conservation, the grid companies should give priority to consumptive hydropower, wind power and other clean electricity to fulfill their social responsibility and promote the carbon emission reduction in power industry. But under the current power purchase mode, grid companies must first perform the contract. This is extremely uneconomical and not environmentally friendly. Based on hedging theory, this paper proposes a power purchase optimization model using the strategy of “compression and compensation”. If outer price is lower than the contract price, the grid can compress contract power appropriately, leaving more space for purchasing electricity;if outer price is not attractive enough, the grid should timely improve contract proportion, compensating the deviations of contract caused by "compression". Based on the strategy of "compression and compensation", it can effectively reduce the abandoned wind and water, enhance the economic and social benefits of provincial power grid.

Consumer’s Purchase Intention towards Luxury Retailer’s Social Media Advertisements —A Case Study of a Shoe Retail—UAE-Dubai Mall

Digital marketing is considered the preferred method comparing to traditional marketing nowadays [1], but is it true for luxury and expensive products? Since the perception prevails that the concepts “luxury” and “digital” are quite incompatible [2], this paper aims to explore the effectiveness of digital marketing advertisements of luxury retailers, while also exploring consumer’s purchasing intent based on the viewing of such promotions. The overall goal is to provide a model of luxury purchase behaviour through harnessing social media. As a constructionist ontology-based researcher, I believe in multiple versions of reality which can evolve based on my target consumer experiences [3]. My epistemological stance is interpretivism with a subjective approach, inductively interacting with consumers to better understand what this “truth” means to them, incorporating textual material to support the analytical interpretations [4]. It is hoped this paper will inform fashion brands marketers about social media practice to achieve creation of purchase intention and ultimately achieve target sales. The paper is useful to both practitioners and academics in the fields of social media marketing and purchase intention. The research provides some initial insights into consumer perspectives of social media ads and online purchase behaviour.