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.
Highlights
With the constant development of personalized recommendation technology and its wide use in mobile commerce, mobile recommender system crops up [1, 2]
It uses “user-personality traits” vector matrix to find the most k similar users who have similar privacy preference. irdly, as shown in Figure 1, our model provides recommendation based on sentiment analysis and privacy concern
It can be seen that STAS has a high rate of precision, recall, and F1 in seven sentiment types for the classification of sentiment. It suggests that STAS can obtain accurate user preferences. It divides text corpus into several sentiment opinion units, which are mined as 2-tuple for more accurate prediction of sentiment analysis. e experimental results show that three kinds of sentiment vocabulary ontology library have more accurate sentiment analysis than comments vocabulary
Summary
With the constant development of personalized recommendation technology and its wide use in mobile commerce, mobile recommender system crops up [1, 2] It provides users with accurate and real-time mobile personalized service (hereafter abbreviated as MPS) [3, 4]. Mobile personalized recommender system can mine implicit interest of users in online reviews, which can fully provide user’s preferences and sentiment polarity over attributes, functions, and experiences of products and services [7]. Since the information is stored in the form of behavior logs, tracks, and transaction data in network, opinion mining technology is required to extract and analyze user’s sentiment tendency and business knowledge hidden in reviews, drawing many mobile commerce service providers’ attention [8, 9]. Since the information is stored in the form of behavior logs, tracks, and transaction data in network, opinion mining technology is required to extract and analyze user’s sentiment tendency and business knowledge hidden in reviews, drawing many mobile commerce service providers’ attention [8, 9]. erefore, it is a hot topic to extract information about user’s sentiment tendency and implicit preference on the basis of text opinion mining to assist personalized recommender system and provide quality mobile personalized service
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