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

Algorithm 4

Collaborative filtering recommend method combining sentiment tendency with user’s personality traits.

Input: Mobile user , recommend service set Service(R), and score matrix of “user-sentiment” and “user-personality traits.”
Output: Top-N recommend services and its score.
(1)	The calculation of user similarity is based on sentiment analysis.
(2)	The calculation of user similarity is based on personality traits integrated with privacy preference.
(3)	It searches the users similar set of target use by using the composite user similarity, which is calculated by the following equation:
It uses fifty percent of cross-validation method to determine the parameters . When , , and when , .
(4)	It predicts the users’ preference and sorts in Top-N to give recommendation:
.