Language Preference Search Result Ranking
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Solution Overview
Problem
Search engines face challenges in providing language-specific search results that accurately reflect user preferences, often returning results in the preferred language while also including relevant content in different languages that may be of interest to users, despite not matching the specified language.
Innovation Solution
The implementation of language preference data storage and generation of language statistics to rank search results based on user preferences, using a formula that computes a language selection weight for each content item, which adjusts its ranking score based on the percentage of users with matching language preferences who clicked on it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines return results strictly in the user's preferred language, then language preference accuracy is improved, but result completeness deteriorates by excluding relevant content in other languages
Solution Approach 1:
The patent applies local quality by differentiating the treatment of search results based on their language properties. Content items are categorized into different groups (preferred language results vs. other language results) and assigned different ranking weights. The system selectively applies language preference filtering only to certain result types while allowing other language content to appear with adjusted ranking, thus maintaining both precision and completeness locally in different result segments.
Solution Approach 2:
The search results are segmented into distinct categories: results in the user's preferred language and results in other languages. This segmentation allows the system to apply different ranking strategies to each segment - strict language matching for preferred language results and relaxed matching with statistical weighting for other language results, thereby resolving the contradiction between precision and completeness.
2Quantity of substance
If search engines include relevant content in different languages, then result completeness is improved, but language preference accuracy deteriorates by including non-preferred language results
Solution Approach 1:
The system changes the ranking parameter by introducing language selection statistics and computing adjusted ranking scores for content items in non-preferred languages. Instead of binary inclusion/exclusion, the patent modifies the ranking parameter to reflect user behavior patterns, allowing relevant foreign language content to appear with dynamically adjusted positions based on statistical evidence of user interest.
Solution Approach 2:
The patent implements feedback mechanisms by using language selection statistics derived from user click behavior to adjust result ranking. The system continuously learns from user interactions with multilingual content and uses this feedback to refine ranking scores, ensuring that included non-preferred language results are those statistically likely to be of interest to the user.
3Reliability
If search engines use complex language statistics and weighting formulas, then result relevance is improved, but system complexity increases
Solution Approach 1:
The system applies self-service by automatically collecting language selection statistics from user click behavior and using these statistics to compute ranking weights without manual intervention. The system serves itself by generating the necessary data (language selection statistics from user interactions) and processing it through automated formulas to adjust result ranking, reducing the need for complex manual configuration while maintaining high relevance.
Data Source
AI summary
Methods, systems and apparatus, including computer program products are described for ranking content items identified by a search engine and delivering corresponding search results. In one aspect, search engine user language preference data is stored in association with user content item selection records. Analysis of the records is performed to identify content items that appeal to users having common language preferences. Query results can be ranked based on the language preference of the current user and/or the user's query and data derived from the selection records.