With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.

Machine Learning is something not many people will fully understand. It's vague because the human mind can't really determine how a computer "reads" and "performs" but simply put, machine learning is the study of statistical models and algorithms that a machine uses. A machine refers to a mobile device or computer. For this purpose, we're referring to smartphones as we look into how it helps Google Play Store users to see and discover new apps that may be relevant to them. DeepMind has been helping Google when it comes to AI (artificial intelligence).

AI and machine learning model architectures developed by Alphabet's DeepMind have substantially improved the Google Play Store's discovery systems, according to Google. In a blog post this morning, DeepMind detailed a collaboration to bolster the recommendation engine underpinning the Play Store, the app and game marketplace that's actively used by over two billion Android users monthly. It claims that as a result, app recommendations are now more personalized than they used to be. In an email, a Google spokesperson told VentureBeat that the new system was deployed this year. It's not the first time the DeepMind team has contributed its expertise to the Android side of Google's business, it's worth noting.

TapReason is coming out of stealth today with a unique twist on monetization. The company uses artificial intelligence to determine the "magic moment" to recommend an app or game to friends and colleagues. The word-of-mouth recommendation platform from the Caesarea, Israel-based startup is a response to the problem of the growing ineffectiveness of advertising, either through saturation or ad blocking. The A.I. has been tested over the last 18 months on more than 300 registered apps and 23 million devices. When the technology judges one of these right moments, TapReason sends an in-app message from a friend to another friend via messaging platforms, such as WhatsApp, Facebook Messenger, Instagram, Slack, LinkedIn, Skype, WeChat, Line, or one of 20 other services. "With mobile-ad click-through rates spiraling downward as users adopt ad blockers to combat overly aggressive advertising, TapReason delivers an ad-free app growth solution based on advanced A.I., marketing automation, and the most trusted form of advertising -- a recommendation from a trusted friend," said Nimrod Elias, CEO and cofounder of TapReason, in a statement.