RANKING MODEL ADAPTATION FOR SEARCHING
First Claim
1. A method for adapting a ranking model, comprising:
- obtaining one or more in-domain ranking models comprising a plurality of feature functions which map a query/URL pair to a first real number relevance score;
obtaining one or more out-domain ranking models comprising a plurality of feature functions which map the query/URL pair to a second real number relevance score;
training the in-domain ranking models and the out-domain ranking models;
assigning respective weighting factors to trained in-domain ranking models and trained out-domain ranking models;
enhancing the weighting factors using in-domain data according to an adaptation method; and
combining the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models to form an adapted in-domain ranking model which maps the query/URL pair to a third real number relevance score.
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Abstract
Search results provided by a search engine (e.g., for the Internet) are improved and/or made more accurate by addressing the limited availability of human labeled training data for certain domains (e.g., languages other than English, within certain date ranges, corresponding to queries over a certain length, etc.). More particularly, a ranking model trained on in-domain data, for which a small amount of human labeled training data (e.g., query/URL pairs) is available (e.g., languages other than English) is adjusted based upon out-domain data, for which a large amount of human labeled training data (e.g., query/URL pairs) is available (e.g., English). Thus, even though the resulting adapted in-domain ranking model is used in the context of in-domain data (e.g., non-English) to provide search results, the search results are improved because they are influenced by an abundance of, albeit out-domain, human labeled training data.
110 Citations
20 Claims
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1. A method for adapting a ranking model, comprising:
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obtaining one or more in-domain ranking models comprising a plurality of feature functions which map a query/URL pair to a first real number relevance score; obtaining one or more out-domain ranking models comprising a plurality of feature functions which map the query/URL pair to a second real number relevance score; training the in-domain ranking models and the out-domain ranking models; assigning respective weighting factors to trained in-domain ranking models and trained out-domain ranking models; enhancing the weighting factors using in-domain data according to an adaptation method; and combining the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models to form an adapted in-domain ranking model which maps the query/URL pair to a third real number relevance score. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system configured to improve a relevance of Web searches for a query comprising:
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a data structure configured to store a plurality of URLs; an adapted in-domain ranking component configured to rank a plurality of query/URL pairs returned in response to the query, the adapted in-domain ranking component comprising a combination of one or more enhanced weighted trained in-domain ranking models and one or more enhanced weighted trained out-domain ranking models; and a processing component configured to operate the adapted in-domain ranking model on candidate URLs from the data structure. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method for adapting a ranking model, comprising:
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obtaining one or more in-domain ranking models comprising a plurality of feature functions which map a query/URL pair to a first real number relevance score; forming one or more out-domain ranking models comprising a plurality of feature functions which map the query/URL pair to a second real number relevance score; training the in-domain ranking models using in-domain training data and training the out-domain ranking models using out-domain training data; assigning respective weighting factors to trained in-domain ranking models and trained out-domain ranking models; enhancing the weighting factors using in-domain data according to an interpolation method comprising at least one of a neural network ranker, a coordinate enhancement method, and the Powell algorithm; and combining the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models to form an adapted in-domain ranking model which maps the query/URL pair to a third real number relevance score.
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Specification