Sponsor
This work was supported by the National Natural Science Foundation of China (no. 61672108).
Published In
Mathematical Problems in Engineering
Document Type
Article
Publication Date
5-30-2020
Subjects
Deformation of surfaces, Short selling (Securities), Multinomial distribution, Forecasting, Mines & mineral resources
Abstract
User interest mining is widely used in the fields of personalized search and personalized recommendation. Traditional methods ignore the formation of user interest which is a process that evolves over time. This leads to the inability to accurately describe the distribution of user interest. In this paper, we propose the interest tracking model (ITM). To add the timing, ITM uses Dirichlet distribution and multinomial distribution to describe the evolutional process of interest topics and frequent patterns, which well adapts to the evolution of user interest hidden in short texts between different time slices. In addition, it is well known that user interest is composed of long-term interest and situational interest including short-term interest and social hot topics. State-of-the-art methods simply regard the users’ long-term interest as the users’ final interest, which makes those unable to completely describe the user interest distribution. To solve this problem, we propose the deformable interest model (DIM) which designs an objective function to combine users’ long-term interest and situational interest and more comprehensively and accurately mine user interest. Furthermore, we present the degree of deformation which measures the subinterest's degree of influence on final interest and propose in DIM the influence real-time update mechanism. The mechanism adaptively updates the degree of deformation through the linear iteration and reduces the degree of dependence of the interest model on training sets. We present results via a dataset consisting of Flickr users and their uploaded information in three months, a dataset consisting of Twitter users and their tweets in three months, and a dataset consisting of Instagram users and their uploaded information in three months, showing that the perplexity is reduced to 0.378, the average accuracy is increased to 94%, and the average NMI is increased to 0.20, which prove better interest prediction.
Rights
Copyright © 2020 Xiaoru Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI
10.1155/2020/4365602
Persistent Identifier
https://archives.pdx.edu/ds/psu/34265
Citation Details
Wang, X., Li, Y., Yu, Z., Li, F., Zhang, H., Cai, Y., & Li, L. (2020). DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model. Mathematical Problems in Engineering, 2020.