scholarly journals Personalized Recommendation for Mobile Internet Wealth Management Based on User Behavior Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangyu Ye ◽  
Mengmeng Chen
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
User Behavior ◽  
Mobile Internet ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
Transaction Data ◽  
Wealth Management ◽  
Other Information ◽  
Management Issues

Economic development has provided good opportunities for the development of securities companies. Similarly, the development of Internet technology has also brought huge opportunities and challenges to the development of securities companies. Aiming at the current wealth management issues in the era of mobile Internet, this article attempts to develop a personalized recommendation approach on the basis of users’ behavioral data analysis. We analyzed and judged the current situation of mobile Internet wealth management using personalized recommendation systems. On the basis of personalized recommendation, we use the user’s interest tags, personalized recommendation technology, and data mining technology to analyze and summarize customer transaction records. This is done through the use of preservation of customer transaction data. By understanding customers’ investment needs, risk preferences, and other information, we can segment customers and provide them with targeted products and services. As a result of the study, a flexible personalized recommendation framework is designed and validated for mobile Internet wealth management services. The effectiveness of the proposed approach is verified through testing of the developed model.

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 User Behavior Identification and Personalized Recommendation Based on Web Data Mining

2021 ◽  
Vol 15 ◽  
pp. 643-650
Author(s):  
Ya Wang
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Matrix Decomposition ◽  
Personalized Recommendation ◽  
K Nearest Neighbor ◽  
Web Data ◽  
Web Data Mining ◽  
User Behavior Analysis ◽  
Model Based

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

scholarly journals RESEARCH ON BEHAVIORAL MOTIVATION INFERENCE METHOD FOR GEOGRAPHICAL SPATIOTEMPORAL LARGE DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 725-729
Author(s):  
J. W. Li ◽  
W. D. Chen ◽  
Y. Ma ◽  
N. Yu ◽  
X. Li ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
User Behavior ◽  
Rapid Development ◽  
Frequent Itemsets ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
User Interest ◽  
Geographic Context ◽  
Spatio Temporal

Abstract. Along with the rapid development of Internet technology, GNSS technology and mobile terminals, a large amount of information including geographical location and time attributes has been generated. Faced with large and complex Internet geospatial data, how to quickly and accurately extract valuable reference information becomes an urgent problem to be solved. And the user's demand for personalized information of recommendation information is getting stronger and stronger, and researching efficient and accurate personalized recommendation system has good application value. In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based on geographic space-time big dataIt is divided into user explicit interest and user implicit interest, and then establishes a scientific and efficient user behavior motivation prediction model based on geographic situation. User interest information can be obtained from explicit interest information, implicit interest information and geographic situation interest information. Geographical environment, geographic location and other related context information. By introducing time factors, it is used to update and improve the user real-time interest model to achieve accurate prediction of user behavior motives under geographic spatio-temporal big data. Use Apriori algorithm to calculate the support and determine the current Frequent itemsets of user interest in geographic context, using frequent itemsets to generate strong association rules, and realizing the analysis of user behavior motives based on geography context. For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. Effective combination of collaborative filtering algorithms and association rules for geographic context-user behavioral interest adaptation.

scholarly journals Implementation of Parallel Algorithm Technology for Time Series Data Mining

2021 ◽  
Vol 2066 (1) ◽  
pp. 012043
Author(s):  
Wei Wang ◽  
Xiaohui Hu ◽  
Mingye Wang ◽  
Yao Du
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Data Analysis ◽  
Parallel Algorithm ◽  
Time Series Data ◽  
Internet Technology ◽  
Series Data ◽  
Data Set ◽  
Time Series Data Mining ◽  
Analysis Tools

Abstract With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded. However, the single-machine serial mode of traditional data mining cannot be directly transplanted to the cloud platform. Only by parallelizing and improving many classic data mining algorithms can the cloud computing platform and data mining be effectively combined. Therefore, it is of great significance to the research and implementation of parallel algorithm technology for time series data mining. The purpose of this paper is to study the research and implementation of parallel algorithm technology for time series data mining. This paper adopts the method of literature data, mathematical statistics, logic analysis and other research methods to study the parallel algorithm technology research and realization of time series data mining, mainly to make useful explorations of time series data mining and visualization technology. It embodies the design ideas of big data analysis tools, and finally reflects the power and market value of data analysis tools through the display of the platform. Research shows that running in the same data set and the same experimental environment, the improved parallel collaborative filtering algorithm ACF in this paper has higher time running efficiency than the parallel algorithm MCF based on the cooccurrence matrix, and in the case of larger data sets, the more obvious the time difference.

scholarly journals Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

2018 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Taqwa Hariguna ◽  
Uswatun Hasanah ◽  
Nindi Nofi Susanti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Processing Data ◽  
Using Data

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer

A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

2017 ◽  
Vol 14 (2) ◽  
pp. 45-66 ◽  
Author(s):  
Mingjun Xin ◽  
Yanhui Zhang ◽  
Shunxiang Li ◽  
Liyuan Zhou ◽  
Weimin Li
Keyword(s):  
User Behavior ◽  
Rapid Development ◽  
Mobile Internet ◽  
Location Based Services ◽  
Internet Technology ◽  
Behavior Prediction ◽  
Recommendation Algorithm ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Pattern Similarity

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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