1. A method, implemented on at least one machine each of which has at least one processor, storage, and a communication platform connected to a network for predicting search results quality, the method comprising the steps of: receiving, via the at least one processor, a search query from a user; obtaining, via the at least one processor, a plurality of search results from each of a plurality of content sources based on the search query, wherein the plurality of search results from each content source is ranked based on their relevance scores with respect to the search query; normalizing, via the at least one processor, a distribution of the relevance scores of the plurality of search results for each of the plurality of content sources in each position of the ranking by building an order-statistic model based on a first set of the plurality of search results from the each content source and by generating estimated relevance scores of a second set of the plurality of search results from the each content source based on the order-statistic model, wherein the first set is different from the second set; computing, via the at least one processor, a metric for each of the plurality of content sources based on the normalized distribution of the relevance scores, wherein the metric indicates a relevance between the respective plurality of search results from the content source and the search query; ranking, via the at least one processor, the plurality of content sources based on the metrics associated with the plurality of content sources; identifying, via the at least one processor, one or more search results from at least one content source that has a higher ranking; and providing, via the at least one processor, the one or more search results to the user as a response to the search query.
1. A method, implemented on at least one machine each of which has at least one processor, storage, and a communication platform connected to a network for predicting search results quality, the method comprising the steps of: receiving, via the at least one processor, a search query from a user; obtaining, via the at least one processor, a plurality of search results from each of a plurality of content sources based on the search query, wherein the plurality of search results from each content source is ranked based on their relevance scores with respect to the search query; normalizing, via the at least one processor, a distribution of the relevance scores of the plurality of search results for each of the plurality of content sources in each position of the ranking by building an order-statistic model based on a first set of the plurality of search results from the each content source and by generating estimated relevance scores of a second set of the plurality of search results from the each content source based on the order-statistic model, wherein the first set is different from the second set; computing, via the at least one processor, a metric for each of the plurality of content sources based on the normalized distribution of the relevance scores, wherein the metric indicates a relevance between the respective plurality of search results from the content source and the search query; ranking, via the at least one processor, the plurality of content sources based on the metrics associated with the plurality of content sources; identifying, via the at least one processor, one or more search results from at least one content source that has a higher ranking; and providing, via the at least one processor, the one or more search results to the user as a response to the search query. 4. The method of claim 1 , wherein the order-statistic model is built based on the relevance scores of the first set and the positions of the first set in the ranking.