A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health <s> A. Recommendation of Apps <s> Third-party applications (or commonly referred to the apps) proliferate on the web and mobile platforms in recent years. The tremendous amount of available apps in app market-places suggests the necessity of designing effective app search engines. However, existing app search engines typically ignore the latent semantics in the app corpus and thus usually fail to provide high-quality app snippets and effective app rankings. In this paper, we present a novel framework named Panorama to provide independent search results for Android apps with semantic awareness. We first propose the App Topic Model (ATM) to discover the latent semantics from the app corpus. Based on the discovered semantics, we tackle two central challenges that are faced by current app search engines: (1) how to generate concise and informative snippets for apps and (2) how to rank apps effectively with respect to search queries. To handle the first challenge, we propose several new metrics for measuring the quality of the sentences in app description and develop a greedy algorithm with fixed probability guarantee of near-optimal performance for app snippet generation. To handle the second challenge, we propose a variety of new features for app ranking and also design a new type of inverted index to support efficient Top-k app retrieval. We conduct extensive experiments on a large-scale data collection of Android apps and build an app search engine prototype for human-based performance evaluation. The proposed framework demonstrates superior performance against several strong baselines with respect to different metrics. <s> BIB001 </s> A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health <s> A. Recommendation of Apps <s> Due to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an app recommendation service. Different from conventional item recommendation where the user interest is the primary factor, app recommendation also needs to consider factors that invoke a user to replace an old app (if she already has one) with a new app. In this work we propose an Actual- Tempting model that captures such factors in the decision process of mobile app adoption. The model assumes that each owned app has an actual satisfactory value and a new app under consideration has a tempting value. The former stands for the real satisfactory value the owned app brings to the user while the latter represents the estimated value the new app may seemingly have. We argue that the process of app adoption therefore is a contest between the owned apps' actual values and the candidate app's tempting value. Via the extensive experiments we show that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation. Furthermore, the best recommendation performance is achieved when the AT model is combined with them. <s> BIB002 </s> A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health <s> A. Recommendation of Apps <s> In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device. <s> BIB003 </s> A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health <s> A. Recommendation of Apps <s> Mobile phones have evolved from communication to multi-purpose devices that assist people with applications in various contexts and tasks. The size of the mobile ecosystem is steadily growing and new applications become available every day. This increasing number of applications makes it difficult for end-users to find good applications. Recommender systems suggesting mobile applications are being built to help people to find valuable applications. Since the nature of mobile applications differs from classical items to be recommended (e.g. books, movies, other goods), not only can new approaches for recommendation be developed, but also new paradigms for evaluating performance of recommender systems are advisable. During the lifecycle of mobile applications, different events can be observed that provide insights into users' engagement with particular applications. This gives rise to new approaches for evaluation of recommender systems. In this paper, we present AppFunnel: a framework that allows for usage-centric evaluation considering different stages of application engagement. We present a case study and discuss capabilities for evaluating recommender engines by applying metrics to the AppFunnel. <s> BIB004
Users tend to use ways provided by vendors to find desirable apps. However, inexperienced users using less than optimal search methods and/or inadequate descriptions of apps may lead to unexpected results and incur associated cost. Thus, several studies endeavor to deal with this issue. The research by Jiang, Vosecky et al. BIB001 provides a semantic-based search and ranking method for Android vendors to resolve problems of poorly organized descriptions of apps and poor ranking results, also claiming that similar circumstances exist among other major vendors. Instead of recommending new apps, Yin, Luo et al. BIB002 consider the needs of replacing an old app with a new one. They used an estimated tempting value of a new app and compared that with actual satisfactory value of the old app recommended for replacement. Lulu and Kuflik BIB003 propose an unsupervised learning algorithm for eliciting descriptions of apps and information from professional blog sites on the Internet to cluster apps and demonstrate them hierarchically for ease of search. Böhmer, Ganev et al. BIB004 argue that the performance of a recommendation system should be evaluated through the life cycle of a user's engagement with a mobile app, reviewing not only the acceptance of new apps by users, but also the stages of viewing, installation, direct usage and long-term usage to get insight into the reactions of user in each step. In addition to previous works, a hybrid social recommendation system which employs tag and context information is purposed in  , whose authors designed an app for users to use in background mode interactively with a recommendation system on the Internet to achieve ideal results and deal with first-rater, cold-start and sparsity problems. However, none of the research analyzed focuses on recommendation of health apps. More specifically, users can only consider the clustering and classification problems of apps according to varied principles (e.g., FDA  ) first. After clustering and classification of apps, a user can then effectively search among apps manually or adopt suggestions from any recommendation system based on the organized hierarchy. In addition, the explicit information given for apps, for instance, descriptions and comments are not enough to be used in the recommendation stage. The major considerations are correctness of relevant health information and potential malicious behavior of these apps. Further investigation before recommending health apps for users is necessary.