Consumers’ Purchase Behavior Preference in E-Commerce Platform Based on Data Mining Algorithm

E-commerce platform can recommend products to users by analyzing consumers’ purchase behavior preference. In the clustering process, the existing methods of purchasing behavior preference analysis are easy to fall into the local optimal problem, which makes the results of preference analysis inaccurate. Therefore, this paper proposes a method of consumer purchasing behavior preference analysis on e-commerce platform based on data mining algorithm. Create e-commerce platform user portrait template with consumer data records, select attribute variables and set value range. This paper uses data mining algorithm to extract the purchase behavior characteristics of user portrait template, takes the characteristics as the clustering analysis object, designs the clustering algorithm of consumer purchase behavior, and grasps the common points of group behavior. On this basis, the model of consumer purchase behavior preference is established to predict and evaluate the behavior preference. The experimental results show that the accuracy rate of this method is 91.74%, the recall rate is 88.67%, and the F1 value is 90.17%, which are higher than the existing methods, and can provide consumers with more satisfactory product information push.

Exploring and Characterizing Patient Multi-Behavior Engagement Trails and Patient Behavior Preference Patterns in the Pathway-Based mHealth Hypertension Self-Management: Analysis of Usage Data (Preprint)

BACKGROUND Hypertension is a long-term medical condition. Mobile health services can help out-of-hospital patients to self-manage. However, not all management is effective, which may be because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear. OBJECTIVE The purpose of this study was to (1) explore patient multi-behavior engagement trails in the pathway-based hypertension self-management; (2) discover patient behavior preference patterns; (3) identify the characteristics of patients with different behavior preferences. METHODS This study included 863 hypertensive patients who generated 295,855 usage records in the mHealth app from December 28, 2016 to July 2, 2020. Markov Chain was used to infer the patient multi-behavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analysis of variances, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns. RESULTS Markov Chain analysis revealed 3 types of behavior transition (one-way transition, cycle-transition, and self-transition) and 4 trails of patient multi-behavior engagement. In perform task trail (PT-T), Patients preferred to start self-management from the states of Task BP (0.29), Task Drug (0.18), and Task Weight (0.20), and spent more time on the Task Food state (35.87s). Some patients entered the states of Task BP (0.20) and Task Drug (0.25) from the Reminder Item state. In result-oriented trail (RO-T), patients spent more energy on the Ranking state (19.66s) compared the Health Report state (13.52s). In knowledge learning trail (KL-T), there was a high probability of cycle-transition (0.47, 0.31) between the states of Knowledge List and Knowledge Content. In support acquisition trail (SA-T), there was a high probability of self-transition in the Questionnaire (0.29) state. K-means analysis discovered 3 patient behavior preference patterns: only PT-T, PT-T and KL-T, and PT-T and SA-T. There were statistically significant associations between the behavior preference pattern and gender, education level, and blood pressure (BP). CONCLUSIONS This study identified the dynamic, longitudinal, and multi-dimension characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions, and pay attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poor-controlled BP were more likely to be highly involved in hypertension health education.

Agent-based Multi-tier SLA Negotiation for Intercloud

The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.

Agent-based Multi-tier SLA Negotiation for Intercloud

The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.

Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis

This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory (LSTM) technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified.

The response of sycanus aurantiacus ishikawa et okajima (hemiptera: reduviidae) to color polymorphisms larvae of spodoptera exigua (hubner) (lepidoptera: noctuidae)

his study aims to evaluate the predatory behavior, preference and functional response of S. aurantiacus in color polymorphism larvae of S. exigua. The predatory behavior and preference test of S. aurantiacus using green and dark brown color larvae of S. exigua. Functional response test was carried out with 5 treatments of larvae population density with 10 replications. The results showed that S. aurantiacus prefers to dark brown larvae of S. exigua than green. The time taken by predators to find the first and subsequent prey was faster in dark brown larvae than in green, both on low or high larval density. The search time for the first prey on dark brown larvae was 22.7 minutes (at high density) and 39.3 minutes (at low density) while those on green larvae were 26.5 minutes and 42.7 minutes. S. aurantiacus showed type-II functional response to the density rate of S. exigua larvae both dark brown and green with the line equation respectively Y = 1,284x / 1 + 0,056x; R2: 0.96; a: 0,107; Th: 0.52 (dark brown larvae) Y = 1.32x / 1 + 0.063x; R²: 0.952; a: 0,109; Th: 0.58 (green larvae).