DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model

Xiaoru Wang,Fu Li,Yueli Li,Lixian Li,Heng Zhang,Zhihong Yu,Yali Cai

doi:10.1155/2020/4365602

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.

Highlights

User interest mining refers to establishing a user interest model by analyzing a large amount of user behavior data.rough user models of high quality, it is able to describe the real interest of users, making it possible to implement personalized services for users
In order to solve the aforementioned problems, this paper proposes a user mining method based on the deformable interest model to adaptively integrate users’ longterm interest and situational interest. e contributions of this paper mainly include the following aspects: (i) For tracking and describing the evolution of user interest, this paper introduces the time dimension and proposes the interest tracking model (ITM), which maps annotated words to the frequent pattern space and uses the Dirichlet distribution and the multinomial distribution to describe the evolutional process of user interest and frequent patterns between different time slices, respectively
(iii) For solving the problem that the influence of longterm interest and situational interest on user interest needs to be updated in real time, this paper proposes the deformable interest model (DIM), which uses the real-time update mechanism to adaptively update the influence of long-term interest, short-term interest, and hot topics on user interest. e real-time update mechanism considers the possibility of interest change and reduces the dependence of the interest model on training sets

Summary

Introduction

User interest mining refers to establishing a user interest model by analyzing a large amount of user behavior data.rough user models of high quality, it is able to describe the real interest of users, making it possible to implement personalized services for users. Describing the distribution of user interest is the core of user interest mining. Erefore, tracking and describing the evolution of user interest is the biggest challenge in describing the distribution of user interest. Erefore, some literature studies [3,4,5,6,7,8,9,10] attempt to introduce time dimension to track the dynamic changes of user interest. E distribution of user interest described by the current dynamic topic models is a Gaussian distribution centered on the superparameter α of the interest distribution of the Mathematical Problems in Engineering aforementioned time slice and cannot adapt to user interest that suddenly changes between different time slices [4] Static topic models are difficult to meet this demand. erefore, some literature studies [3,4,5,6,7,8,9,10] attempt to introduce time dimension to track the dynamic changes of user interest. e distribution of user interest described by the current dynamic topic models is a Gaussian distribution centered on the superparameter α of the interest distribution of the Mathematical Problems in Engineering aforementioned time slice and cannot adapt to user interest that suddenly changes between different time slices [4]

Methods

Results

Conclusion