Patent application number | Description | Published |
20110087655 | Search Ranking for Time-Sensitive Queries by Feedback Control - In one embodiment, a method comprises accessing a search query received at a search engine; identifying a plurality of network resources for the search query; calculating a ranking score for each of the network resources; determining whether the search query is year-qualified; and if the search query is year-qualified, then adjusting the ranking scores of selected ones of the network resources based on a difference between the ranking score of an oldest one of the network resources and the ranking score of a newest one of the network resources and a confidence score representing a likelihood that the search query is year-qualified. | 04-14-2011 |
20110093459 | Incorporating Recency in Network Search Using Machine Learning - In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features. | 04-21-2011 |
20110231380 | SESSION BASED CLICK FEATURES FOR RECENCY RANKING - In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q | 09-22-2011 |
20110231390 | SESSION BASED CLICK FEATURES FOR RECENCY RANKING - In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature. | 09-22-2011 |
20120016877 | CLUSTERING OF SEARCH RESULTS - One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters. | 01-19-2012 |
20120116915 | Mobile-Based Real-Time Food-and-Beverage Recommendation System - Particular embodiments extract a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, each of the plurality of establishments sells food or beverage; construct a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items; generate a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items; and compute one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two users, two establishments, two items, one user and one establishment, one of user and one item, or one establishment and one item. | 05-10-2012 |
20120150855 | CROSS-MARKET MODEL ADAPTATION WITH PAIRWISE PREFERENCE DATA - Embodiments are directed towards generating market-specific ranking models by leveraging target market specific pairwise preference data. The pairwise preference data includes market-specific training examples, while a ranking model from another market captures the common characteristics of the resulting ranking model. In one embodiment, the ranking model is trained by applying a Tree Based Ranking Function Adaptation (TRADA) algorithm to multi-grade labeled training data, such as editorially generated training data. Then, contradictions between the TRADA generated ranking model and target-market specific pairwise preference data are identified. For each identified contradiction, new training data is generated to correct the contradiction. Then, in one embodiment, an algorithm such as TRADA is applied to the existing ranking model and the new training data to generate a new ranking model. | 06-14-2012 |
20120271842 | LEARNING RETRIEVAL FUNCTIONS INCORPORATING QUERY DIFFERENTIATION FOR INFORMATION RETRIEVAL - The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries. | 10-25-2012 |
20120316955 | System and Method for Mobile Application Search - Method, system, and programs for providing adaptive application searching are disclosed. An application search request relevant to a user is received. First information associated with the user and second information associated with a plurality of applications is obtained. At least one application of the plurality of applications is identified as of interest based on the application search request, the first information, and the second information. The at least one application is provided in response to the application search request. | 12-13-2012 |
20130132401 | RELATED NEWS ARTICLES - Methods, systems, and computer programs are presented for providing internet content, such as related news articles. One method includes an operation for defining a plurality of candidates based on a seed. For each candidate, scores are calculated for relevance, novelty, connection clarity, and transition smoothness. The score for connection clarity is based on a relevance score of the intersection between the words in the seed and the words in each of the candidates. Further, the score for transition smoothness measures the interest in reading each candidate when transitioning from the seed to the candidate. For each candidate, a relatedness score is calculated based on the calculated scores for relevance, novelty, connection clarity, and transition smoothness. In addition, at least one of the candidates is selected based on their relatedness scores for presentation to the user. | 05-23-2013 |