The skyrocketing growth of mobile apps and mobile devices has significantly fueled the competition among app developers. They have leveraged the app store capabilities to analyse app data and identify app improvement opportunities. Existing research has shown that app developers mostly rely on in-domain (i.e., same domain or same app) data to improve their apps. However, relying on in-domain data results in low diversity and lacks novelty in recommended features. In this work, we present an approach that automatically identifies, classifies and ranks relevant popular features from cross-domain apps for recommendation to any given target app. It includes the following three steps: 1) identify cross-domain apps that are relevant to the target app in terms of their features; 2) filter and group semantically the features of the relevant cross-domain apps that are complementary to the target app; 3) rank and prioritize the complementary cross-domain features (in terms of their domain, app, feature and popularity characteristics) for adoption by the target app’s developers. We have run extensive experiments on 100 target apps from 10 categories over 15,200 cross-domain apps from 31 categories. The experimental results have shown that our approach to identifying, grouping and ranking complementary cross-domain features for recommendation has achieved an accuracy level of over 89%. Our semantic feature grouping technique has also significantly outperformed two existing baseline techniques. The empirical evaluation validates the efficacy of our approach in providing personalised feature recommendation and enhancing app’s user serendipity.

Competition in mobile applications (i.e., apps) is becoming more and more intense with the increase in popularity of smart phones and mobile devices. Previous research shows that app developers spent considerable amount of time in exploiting user feedback to improve their apps. However, relying on own user feedback is insufficient for app survival in such competitive environment. It is highly important for an app developer to learn from competitors in order to keep the rank higher or become topper in the store (e.g., Google Play1). In this work, we present an approach to automatically classify and rank popular and un-popular features from the competitor apps. We follow 3 steps- (1) extract features from competitor app updates (i.e., whatsNew) and user feedback (i.e, reviews), (2) filter and group the review features that are relevant to whatsNew features, then, classify whatsNew features to binary classes, such as, popular and unpopular, (3) rank and prioritize those popular and unpopular features from competitors in order to recommend developers to adopt or avoid those features. The ranking of whatsNew features are done based on whatsNew-to-review relevance, user sentiments, and popularity of those features. We conduct extensive experiments on 840 updates and 262000 reviews of 84 different competitor apps of 10 categories. We found encouraging results from the experiments and the empirical evaluation validates the efficacy of our approach, hence, potential usefulness to the developers.