Adaptive Search Word Recommendation System
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Solution Overview
Problem
Existing search systems face challenges in providing immediate and accurate recommendations for related search words due to limitations in initial data, reliance on historical logs, and difficulties in weight combination and vertical domain conversion, leading to suboptimal recommendations.
Innovation Solution
A system and method that adaptively adjust the search process based on the number of accumulated search logs, using a combination of content-based and log-based approaches to recommend related search words, with thresholds set for cumulative search counts to evolve from initial to late-stage processes, optimizing the search process to provide suitable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If content-based approach is used to provide related search word recommendations, then immediate recommendations can be provided at the initial stage, but the recommendations cannot analyze and predict user search intent accurately
Solution Approach 1:
The system dynamically switches between content-based and log-based approaches based on the accumulation of search logs. At the initial stage with limited logs, content-based methods provide immediate responses. As search logs accumulate, the system transitions to log-based methods for improved intent prediction, and finally uses a hybrid approach combining both methods for optimal performance.
Solution Approach 2:
The system changes the operational parameters of the search recommendation mechanism based on the quantity of accumulated search logs. Thresholds are defined for cumulative search counts to determine when to switch from content-based to log-based or hybrid approaches, allowing the system to adapt its behavior based on data availability.
2Measurement precision
If log-based approach is used to predict user search intent, then better recommendation accuracy can be achieved, but the system cannot provide immediate recommendations at the initial stage and requires long-term data accumulation
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing content data before it is needed for search recommendations. This allows the content-based approach to be ready for immediate use at the initial stage, while the system simultaneously accumulates search logs for later use in log-based or hybrid approaches.
Solution Approach 2:
The system dynamically adapts its recommendation strategy based on the maturity of the search log database. It transitions from content-based methods when logs are insufficient to log-based or hybrid methods when adequate logs are accumulated, ensuring both immediate responsiveness and long-term accuracy.
3Adaptability or versatility
If weight combination method is used to integrate content-based and log-based approaches, then suitable recommendations can be provided at different stages, but manual setting of weights is difficult and requires sufficient historical data
Solution Approach 1:
The system performs self-optimization by automatically adjusting the weight combination based on the quantity of accumulated search logs. Instead of requiring manual weight configuration, the system uses thresholds for cumulative search counts to automatically determine when to switch between content-based, log-based, or hybrid approaches, making the system self-adaptive to different operational stages.
Data Source
AI summary
A system for adaptively adjusting related search words are provided. The system includes an input device, a search log collection module, a threshold setting module and a process evolution module. The input device receives a search word. The search log collection module determines whether the cumulative search count of the search word is greater than a first threshold or less than a second threshold. The threshold setting module sets the first threshold and the second threshold in terms of the number of search logs. When the cumulative search count of the search word is between the first threshold and the second threshold, the process evolution module optimizes the middle search process to find out at least one related word and/or at least one historical search word most related to the attributes or content of the search word from the indexed text and the historical search log.