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.
#TAGGER APP TRIAL#
See many more features here Click here for a free trial or if you are looking for a super simple fully automated tagger, why not take a look at SongKong.Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app.
Jaikoz offers unlimited usage forever and free email and forum support Jaikoz supports Multiple Audio formats, so you only need one application for tagging your audio files Jaikoz fixes your artwork as well as your textual data Jaikoz saves you time by accurately autofixing your files unattended from MusicBrainz and Discogs
#TAGGER APP FOR MAC OS#
Jaikoz is available for Mac OS X, Windows and Linux.Ī free trial is available now Top Reasons to buy Jaikoz:
The latest release is Jaikoz 11.5.2 Sebadoh, this was released on July 13th 2022 The current release supports tagging of Mp4, M4a, M4p, Mp3, Wma, Flac, Aiff, Wav, Dsf and Ogg files. It as quick and easy as possible to edit your data manually as well using a convenient spreadsheet view, with many autoformatting features. But no identification system is 100% accurate so we have made You the flexibility to lookup your songs by both the acoustic id and the metadata making Jaikoz a very accurate tool. Many of the songs also have an Acoustic Id provided by Acoustid, allowing a song to be identified by the actual music, so it can do a match even if you have no metadata! These feature means that Jaikoz gives Jaikoz uses MusicBrainz, an online database of over eleven million songs and Discogs another database of over 4 million releases. Powerful yet simple to use tool that allows you to organize, edit and correct thousands of these tags with ease. Are you frustrated by missing information in your audio files? This is known as metadata and is stored in a Tag.
0 kommentar(er)
