App Search Engine Leveraging User Reviews for Cold Start
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The vast number of mobile apps in app stores makes it difficult for users to find specific apps without search or recommendation functions, and existing recommendation systems are less accurate and face challenges with cold start problems, as they rely on user profiles and transaction data.
Innovation Solution
A method and system for an app search engine that leverages user reviews by receiving a search query, extracting and preprocessing app descriptions and reviews, creating a topic-based language model, and ranking apps based on relevance to provide a ranked list to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of mobile apps in app stores increases, then the variety and availability of apps improve, but it becomes extremely difficult for users to find specific apps
Solution Approach 1:
The patent introduces an app search engine as an intermediary system between users and the vast number of apps in app stores. This search engine processes user queries, analyzes app descriptions and user reviews, and retrieves relevant apps, thereby mediating the connection between users and the large volume of available apps without requiring users to manually search through all apps
Solution Approach 2:
The patent replaces manual mechanical searching through app lists with an automated information retrieval system. The search engine automatically processes user queries, compares them against app descriptions and reviews, and generates ranked results, substituting the manual browsing process with an automated computational system that scales with the number of apps
2Ease of operation
If recommendation systems use user profiles and transaction data to suggest apps, then convenience for users improves, but accuracy decreases and cold start problems occur
Solution Approach 1:
The patent enables users to actively express their own needs through search queries, replacing the passive profile-based recommendation approach. Users directly input what they are looking for, and the search engine retrieves apps matching those explicit requirements, allowing users to self-serve their own recommendation needs without relying on system-generated profiles
Solution Approach 2:
The patent inverts the traditional recommendation approach by switching from system-generated recommendations based on user profiles to user-initiated searches based on explicit queries. Instead of the system guessing what users need based on past behavior, users directly state their needs through search queries, and the system responds by retrieving matching apps
3Ease of operation
If app search relies on app names only, then search simplicity improves, but search accuracy decreases since 83% of app searches are made by function
Solution Approach 1:
The patent merges multiple information sources including app descriptions, user reviews, and query terms into a unified search analysis process. The system combines these different text sources to generate comprehensive search results, integrating both structured app metadata and unstructured user-generated content to improve search accuracy while maintaining simplicity
Data Source
AI summary
A method for an app search engine leveraging user reviews is provided. The method includes receiving an app search query from a user, determining a plurality of relevant apps based on the received app search query, and extracting app descriptions and user reviews associated with the plurality of relevant apps from an app database. The method also includes preprocessing the extracted app descriptions and user reviews of each of the plurality of relevant apps to generate a text corpus and creating a topic-based language model for each of the plurality of relevant apps based on the generated text corpus. Further, the method includes ranking a list of relevant apps using the topic-based language model and providing the ranked app list for the user.