TY - GEN
T1 - Recommendations for mobile applications
T2 - 1st International Conference on Internet of Things and Machine Learning, IML 2017
AU - Pai, Hao Ting
AU - Lai, Hung Wei
AU - Wang, Shu Li
AU - Wu, Mei Fang
AU - Chuang, Yung-Ting
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/17
Y1 - 2017/10/17
N2 - In the last decade, information and communication technologies have been highly developed. For convenience, many applications have been installed in smartphones instead of desktop computers. As a popular platform, Google Play presents thousands of mobile applications. Because there are so many dazzling applications, it is difficult for users to determine which are suitable for their needs. Many factors are likely to influence the purchase of an application, such as advertisements, word of mouth, and other media. In deciding whether to purchase an application, users probably refer to customer reviews. Indeed, users may take a significant amount of time to evaluate the legitimacy of the reviews. In this paper, we introduce a concept for recommending applications. Based on pointwise mutual information, we calculate the positive or negative score of semantic orientation in each review. We also consider subjective factors (i.e., public opinion, anonymous opinion, and star rating) and objective factors (i.e., download number and reputation).
AB - In the last decade, information and communication technologies have been highly developed. For convenience, many applications have been installed in smartphones instead of desktop computers. As a popular platform, Google Play presents thousands of mobile applications. Because there are so many dazzling applications, it is difficult for users to determine which are suitable for their needs. Many factors are likely to influence the purchase of an application, such as advertisements, word of mouth, and other media. In deciding whether to purchase an application, users probably refer to customer reviews. Indeed, users may take a significant amount of time to evaluate the legitimacy of the reviews. In this paper, we introduce a concept for recommending applications. Based on pointwise mutual information, we calculate the positive or negative score of semantic orientation in each review. We also consider subjective factors (i.e., public opinion, anonymous opinion, and star rating) and objective factors (i.e., download number and reputation).
KW - Data mining
KW - Opinion mining
KW - Semantic orientation
UR - http://www.scopus.com/inward/record.url?scp=85048355212&partnerID=8YFLogxK
U2 - 10.1145/3109761.3109771
DO - 10.1145/3109761.3109771
M3 - Conference contribution
AN - SCOPUS:85048355212
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017
A2 - Hamdan, Hani
A2 - Hidoussi, Faouzi
A2 - Boubiche, Djallel Eddine
PB - Association for Computing Machinery
Y2 - 17 October 2017 through 18 October 2017
ER -