Mobile Personalized Service Recommender Model Based on Sentiment Analysis and Privacy Concern

Abstract

The existing mobile personalized service (MPS) gives little consideration to users’ privacy. In order to address this issue and some other shortcomings, the paper proposes a MPS recommender model for item recommendation based on sentiment analysis and privacy concern. First, the paper puts forward sentiment analysis algorithm based on sentiment vocabulary ontology and then clusters the users based on sentiment tendency. Second, the paper proposes a measurement algorithm, which integrates personality traits with privacy preference intensity, and then clusters the users based on personality traits. Third, this paper achieves a hybrid collaborative filtering recommendation by combining sentiment analysis with privacy concern. Experiments show that this model can effectively solve the problem of MPS data sparseness and cold start. More importantly, a combination of subjective privacy concern and objective recommendation technology can reduce the influence of users’ privacy concerns on their acceptance of MPS.

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

“…Step1: It calculates the path coefficient of the influenced factors of privacy concerns based on structural equation model [15], and defines its absolute value as privacy concerns intensity I u,p j . The i u,p j means the value of users' preference, influenced by one of privacy concern factors (user's privacy tendency, internal locus of control, openness, extroversion, easygoingness, and social group influence), which has four dimensions (information collection, improper access, information error, and secondary use).…”

“…This paper extracts 400 sample sets from questionnaire survey data [15]. The total number of survey data is 421, and its descriptive statistical analysis is listed in Table 1.…”

“…According to our previous research, the adoption of MPRS is significantly constrained by six privacy concern factors-those being the user's privacy tendency, internal locus of control, openness, extroversion, easygoingness, and social group influence. These influenced the user's privacy concerns relating to areas including determining information collection, improper access, information error, and secondary use, and ultimately influenced their willingness to adopt of MPRS [15]. On this basis, the paper proposes a mobile personalized recommendation model based on privacy concerns and context analysis (PC-MPRS) to solve the problems of how to measure the intensity of privacy concerns, the definition and expression of complex multi-dimensional contexts, and improving recommendation methods incorporating privacy concerns with context information.…”

Understanding the influence of privacy protection functions on continuance usage of push notification service

“…With the rapid development of big data technology, PNS emerges in various fields, such as social networks, e-business and social commerce (Lin et al , 2017; Xiao et al , 2018). It provides users with accurate and real-time contents that are specifically context relevant.…”

Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering

“…Xiao et al [20] have recommended and proposed an estimation calculation, which coordinates identity characteristics with protection inclination power and afterward groups the clients according to identity attributes. Next, this paper accomplishes a cross-breed communitarian separating proposals by joining supposition examination with protection concern.…”