Rank-Adjusted Content Items via Query Path Mining
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Solution Overview
Problem
Search engines often fail to identify and rank content items relevant to a user's current interests until multiple searches are conducted, as they rank content independently for each query, leading to missed opportunities for relevant content presentation.
Innovation Solution
The system processes click logs and query logs to identify statistical search patterns, compares search sessions to these patterns, and adjusts content item rankings based on query paths and context, using a mining engine to mine query paths and content terminuses, and an adjusting engine to refine rankings based on user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines identify and rank content items independently for each query, then the search process is simple and fast, but relevant content items are not identified until multiple searches are conducted
Solution Approach 1:
The system performs preliminary actions by analyzing click logs and query logs to identify statistical search patterns and query paths before actual search queries are submitted. This pre-computation of user behavior patterns enables the system to predict relevant content items in advance, so when a user submits a query, the system can immediately present relevant content without requiring multiple sequential searches.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user click behavior and using this feedback to refine and update statistical search patterns and query paths. The click logs provide feedback about which content items users actually select, allowing the system to learn from user interactions and improve its ability to identify relevant content items in future searches.
2Productivity
If search engines process each query independently, then the system complexity is low, but user experience and search efficiency deteriorate
Solution Approach 1:
The system segments the search process into distinct functional components: a mining engine that processes logs to identify patterns, an adjusting engine that modifies content rankings based on detected patterns, and the core search engine that presents results. This segmentation allows each component to specialize in a specific task, improving overall search efficiency while managing system complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary components between the user query and the content database. The mining engine acts as an intermediary that pre-processes log data to extract statistical patterns, and the adjusting engine serves as another intermediary that uses these patterns to modify content rankings before presentation. These intermediaries enhance search efficiency by adding intelligent filtering and ranking layers without requiring complete system redesign.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a context of the current search session. In one aspect, a method includes identifying query paths from previous search sessions, each query path defining a context and being a plurality of queries in an order in which the queries were provided for in a respective previous search session; identifying search session queries of a current search session; comparing the search session queries of the current search session to the queries in the query paths from the previous search sessions; and determining that a context of the current search session is related to a query path from the previous search sessions based at least in part on the comparison, the determining including: determining that two or more of the queries of the query path are similar to two or more of the search session queries.