Application Spam Detection via Developer Source Object Analysis

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Solution Overview

Problem

The increasing demand for applications in an app-centric world has led to a surge in spam applications that provide minimal functionality, making it difficult for users to find relevant and trustworthy applications through search engines, as these spam applications aim to gain access to sensitive user information or sell themselves, and existing technologies lack effective methods to filter out such applications.

Innovation Solution

An application search engine system that identifies potential spam applications by analyzing developer features, such as the number of source objects and feedback units, and applies penalties by removing or reducing the visibility of these applications in search results, ensuring that only relevant and trustworthy applications are displayed to users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the number of applications in the search engine increases to meet user demand, then the variety and availability of applications improve, but the presence of spam applications increases making it difficult for users to find trustworthy applications

Engineering Contradiction:

Improvenumber of applicationsVSAvoidtrustworthiness of applications

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system performs preliminary analysis of developer features (number of source objects, feedback units) before applications are ranked in search results. By pre-calculating spam indicators based on developer history and application feedback, the system proactively identifies and penalizes potential spam applications before they can negatively impact user experience, thus maintaining reliability while allowing application quantity to grow

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system utilizes feedback units (user reviews, ratings, complaints) as a key metric to assess application quality and developer behavior. This feedback mechanism creates a closed loop where user experiences directly influence the spam detection algorithm, allowing the system to continuously adapt and improve its ability to distinguish trustworthy applications from spam while the overall application ecosystem expands

Inventive Principle:

Principle #23Feedback

2Loss of information

If spam applications are allowed to remain in search results to maintain comprehensive search coverage, then the completeness of search results is preserved, but user experience deteriorates due to exposure to malicious or low-quality applications

Engineering Contradiction:

Improvecompleteness of search resultsVSAvoiduser exposure to malicious applications

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The system applies differentiated treatment to different applications based on their individual spam risk profiles. Rather than uniformly filtering all applications or allowing all to remain visible, the system locally adjusts the visibility and ranking of each application based on its developer's feedback units and source object count. This allows comprehensive search coverage while selectively protecting users from harmful applications through targeted penalty application

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The spam detection system acts as an intermediary layer between the complete application database and the user-facing search results. This intermediary component analyzes developer features and applies penalties to potentially malicious applications, serving as a buffer that maintains information completeness in the backend while filtering out harmful content from the front-end user experience

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If strict filtering of spam applications is applied to improve search quality, then user experience and search accuracy improve, but the number of legitimate applications that may be incorrectly filtered increases

Engineering Contradiction:

Improveaccuracy of spam detectionVSAvoidfalse positive rate

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The system uses multiple parameters (number of source objects, feedback units, developer history) rather than a single threshold to determine spam status. By changing from a binary filter approach to a multi-parameter scoring system with penalization, the system achieves more nuanced and accurate spam detection that reduces false positives while maintaining high precision in identifying actual spam applications

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9794284B2Application spam detector

Publication Date: 2017.10.17 SAMSUNG ELECTRONICS CO LTD

AI summary

A search engine includes a network interface that receives a search query and a search module. The search module determines a consideration set of applications corresponding to the search query based on application data stored for a plurality of applications, determines a respective number of source objects associated with each of the applications in the consideration set, determines whether each of the applications is a spam application based on the respective number of source objects associated with each of the applications, applies respective penalties to selected ones of the applications based on the determination of whether each of the applications is a spam application, generates search results based on the respective penalties applied to the selected ones of the applications, and provides the search results to be transmitted by the network interface.