#Tagger app android

Extensive evaluations demonstrate that CAP achieves 30% higher accuracy than a state-of-the-art method Personalized Ranking Metric Embedding (PRME) in terms of In terms of mean reciprocal rank (MRR), CAP achieves 1.5× higher than the straightforward baseline Sta and 2× higher than PRME. We evaluate the performance of our proposed CAP with two large-scale real-world datasets. In addition, we create a user profile for each user with App usage and trajectory history to describe the individual dynamic preference for personalized prediction. We find that the relationships between App-location, App-time, and App-App type are essential to prediction and propose a heterogeneous graph embedding algorithm to map them into the common comparable latent space. This paper presents CAP, a context-aware App usage prediction algorithm that takes both contextual information (location & time) and attribution (App with type information) into consideration. Prediction becomes more difficult when the user appears at a new location. A single user's data is sparse on the spatial domain and only covers a limited number of locations. It is difficult to describe individuals' preferences, which are usually time-variant. App usage behavior is usually influenced by multiple factors, especially temporal and spatial factors. However, it is still a challenging problem because of the following three aspects: i. Prior works have explored this topic through individual data profiles and contextual information. The results of extensive experiments with three real-world datasets and a dataset constructed by human experts demonstrate the effectiveness of SimAndro.Ĭontext-aware mobile application (App) usage prediction benefits a variety of applications such as precise bandwidth allocation, App launch acceleration, etc. To compute the similarity score of an app-pair, a separate similarity score is computed based on each feature, and a weighted linear combination of these four scores is regarded as the final similarity score by utilizing an automatic weighting scheme based on TreeRankSVM. SimAndro performs both feature extraction and similarity computation where the API calls, manifest information, package name, and strings are used as features.

#Tagger app android

In this paper, we propose an effective method called SimAndro to compute the similarity of apps, which extracts the features based on the information obtained only from apps themselves and the Android platform without using information obtained from third-party sources such as app stores. Furthermore, all the existing methods use the features obtained only from the app stores such as description and rating, which could be inaccurate, varied in different stores, and affected by language barrier they totally neglect useful information clearly capturing the app’s functionalities and behaviors that can be mined from the apps themselves such as the API calls and manifest information. Contrary to malware detection, very fewer efforts have been devoted to similarity computation of apps. Therefore, there is an important demand for app search engines or recommendation services where developing an accurate similarity method is a challenging issue.

The encouraging results demonstrate that our technique is effective and promising.Īs the number of Android applications (apps) is increasing dramatically, users face a serious problem to find relevant apps to their needs. To evaluate the efficacy of our proposed framework, we conduct an extensive set of experiments on a large real-world dataset crawled from Google Play. Specifically, given a novel query app without tags, our proposed framework (i) first explores online kernel learning techniques to retrieve a set of top-N similar apps that are semantically most similar to the query app from a large app repository and (ii) then mines the text data of both the query app and the top-N similar apps to discover the most relevant tags for annotating the query app. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app.