Smart Match Autocomplete System for Content Title Optimization
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Solution Overview
Problem
Online content publication platforms face challenges in retrieving relevant content due to inadequate title descriptions and insufficient detail in user queries, leading to unviewed content and low conversion rates for sellers, as first-time or occasional users struggle to create detailed titles that align with user interests.
Innovation Solution
The Smart Match Autocomplete (SMAC) system assists users in creating high-quality content descriptions by analyzing data to identify important keywords and aspects, providing prompts for inclusion in titles through an orchestration engine, category recognition module, autocomplete module, and aspect extraction module, ensuring better keyword and description usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users create simple titles for content, then ease of operation is improved, but search retrieval effectiveness deteriorates
Solution Approach 1:
The system performs preliminary analysis of the content item and automatically generates suggested titles and keywords before the user completes the posting process. The orchestration engine 102 analyzes the content 308 and generates suggested titles 314, incorporating important keywords 304 based on category 306 and aspect 310 information, so that users can easily select from pre-generated options rather than creating titles from scratch
Solution Approach 2:
The system enables self-service by automatically extracting key information from the content 308 and generating optimized titles 314 and keyword suggestions 304 without requiring users to manually research or craft detailed titles. The aspect extraction module 114 and category recognition module 108 automatically identify and incorporate relevant terms, allowing users to benefit from automated optimization while maintaining ease of use
2Reliability
If the content publication platform provides detailed search methodologies, then search retrieval effectiveness is improved, but device complexity increases
Solution Approach 1:
The search optimization function is segmented into specialized modules: category recognition module 102 for identifying content categories 306, aspect extraction module 114 for extracting key aspects 310, autocomplete module 112 for suggesting keywords 304, and orchestration engine 102 for coordinating these functions. This segmentation allows complex search optimization to be achieved through multiple simple, focused components rather than one complex system
Solution Approach 2:
The orchestration engine 102 acts as an intermediary that coordinates between the category recognition module 102, aspect extraction module 114, autocomplete module 112, and the search system 328. It integrates information from category 306, aspect 310, and keyword 304 analysis to generate optimized search queries, simplifying the overall system architecture by providing a central coordination layer
Data Source
AI summary
Aspects of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and computer-implemented methods for providing suggestions of additional input to users entering user input into a data input field. In some embodiments, the method includes receiving initial user input entered via a data input field of a user interface rendered on a client device, and identifying a dominant category corresponding to the initial user input. The method further includes identifying a set of aspects corresponding to the dominant category, and selecting, from the set of aspects, a set of aspect suggestions based on a ranking of each aspect in the set of aspects. The method further includes causing display of a suggestion box presented in conjunction with the data input field. The suggestion box comprises a presentation of the set of aspect suggestions.