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Liu, Chun; Yang, Wei; Li, Zheng and Yu, Yijun
(2020).
DOI: https://doi.org/10.1002/spe.2845
Abstract
With lots of public software descriptions emerging in the application market, it is significant to extract common software features from these descriptions and recommend them to new designers. However, existing approaches often recommend features according to their frequencies which reflect designers’ preferences. In order to identify those users’ favorite features and help design more popular software, this paper proposes to make use of the public data of users’ ratings and products’ downloads which reflect users’ preferences to recommend extracted features. The proposed approach distinguishes users’ perspective from designers’ perspective and argues that users’ perspective is better for recommending features because most products are designed for users and expect to be popular among users. Based on the lasso regression to estimate the relationship between the extracted features and the users’ ratings, it proposes to first distinguish the extracted features to identify those rec- ommendable and undesirable features. By treating each download as a support from users to the product features, it further mines the feature association rules from users’ perspective for recommending features. By taking the public data on the market of SoftPedia.com for evaluation, our empirical studies indicate that: (1) selecting recommendable features by lasso regression is better than that by feature frequencies in terms of F1 measure; and (2) recommending features based on the feature association rules mined from users’ perspective is not only feasible but also has competitive performance compared with that based on the rules mined from designs’ perspective in terms of F1 measure.
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