User Behavior Identification and Personalized Recommendation Based on Web Data Mining

Abstract

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.

Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

“…In expression (13), T r represents the normal length of testing; T m represents the maximum length of testing. Expression (13) shows that when the length of testing is shorter than the normal length, it can be deemed that the user's cognitive level is higher, and the value is set as 1; on the contrary, when the length of testing is longer than the maximum length, it can be deemed that the user's cognitive level is relatively lower, and the value is set as 0. Likewise, the relationship between user's cognitive level and the length of online testing can be achieved.…”

“…At the same time, it is also found that although many researchers have proposed various ideas for building the ALS system framework, their research results or conclusions are still being explored and improved. For example, some researchers focus on recommendation algorithms, adaptive engines, or data mining, but their thinking on the user model of ALS system is not comprehensive and in-depth enough, which leads to the unsatisfactory recommendation effect of the ALS system [ 13 ]. Based on this, we believe that it is necessary to conduct a comprehensive and in-depth study on the user feature model of the ALS system.…”