Abstract: A server determines a plurality of immediate candidate items for a first web page to recommend to a user. For each particular immediate candidate item of the plurality of immediate candidate items, the server determines a separate sequence of two or more subsequent possible candidate items for subsequent web pages to recommend to the user in the event that the user selects the particular immediate candidate item. Further, the server selects a particular immediate candidate item from the plurality of immediate candidate items for the first web page to recommend to the user. The first web page that recommends the plurality of immediate candidate items is generated and sent over the Internet to the user.

Abstract: Example methods, apparatuses, and articles of manufacture are disclosed that may be used to provide or otherwise support one or more ranking specialization techniques for use with search engine information management systems.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs, determining preference data for the one or more query-content item pairs of the one or more training sets and determining labeled data for the one or more query-content item pairs of the one or more training sets. A ranking function is determined based upon the preference data and the labeled data for the one or more content-item query pairs of the one or more training sets. The ranking function is then stored for application to query-content item pairs not contained in the one or more training sets.

Abstract: An information retrieval system is described herein that monitors a microblog data stream that includes microblog posts to discover and index fresh resources for searching by a search engine. The information retrieval system also uses data from the microblog data stream as well as data obtained from a microblog subscription system to compute novel and effective features for ranking fresh resources which would otherwise have impoverished representations. An embodiment of the present invention advantageously enables a search engine to produce a fresher set of resources and to rank such resources for both relevancy and freshness in a more accurate manner.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems are disclosed that relate to ranking functions for multiple different domains. By way of example but not limitation, ranking functions for multiple different domains may be trained based on inter-domain loss, and such ranking functions may be used to rank search results from multiple different domains so that they may be blended without normalizing relevancy scores.

Abstract: 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.

Abstract: To estimate, or predict, the relevance of items, or documents, in a set of search results, relevance information is extracted from user click data, and relational information among the documents as manifested by an aggregation of user clicks is determined from the click data. A supervised approach uses judgment information, such as human judgment information, as part of the training data used to generate a relevance predictor model, which minimizes the inherent noisiness of the click data collected from a commercial search engine.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated, and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets.

Abstract: A method and system for blending ranking for an output display includes receiving a first list of content items having a first ranking determined by first ranking parameters, the first ranking providing for a sequential ordering of the content items of the first list. A second list of content items having a second ranking determined by second ranking parameters are received, the first ranking is incompatible with the second ranking because ranking parameters are different. The first list of content items is transformed to a modified first list that maintains the order of the content items and makes the first ranking of the modified first list compatible with the second ranking of the second list. The second list and the modified first list are merged to generate a blended list for an output display utilizing the blended list.

Abstract: The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.

Abstract: Exemplary methods and apparatuses are provided which may be used to establish a ranking function or the like, which may be used by a search engine or other like tool to search a related-task search domain.

Abstract: System and method for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporates click-through features of the content items. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating training set having one or more query-URL pairs labeled for relevance based on their click-through features. The labeled query-URL pairs are used to determine the relevance function by minimizing a loss function that accounts for click-through features of the content item. The computed relevance function is then applied to the click-through features of unlabeled content items to assign relevance scores thereto. An inverted click-through index of query-score pairs is formed and combined with the content index to improve relevance of search results.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated. and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs, determining preference data for the one or more query-content item pairs of the one or more training sets and determining labeled data for the one or more query-content item pairs of the one or more training sets. A ranking function is determined based upon the preference data and the labeled data for the one or more content-item query pairs of the one or more training sets. The ranking function is then stored for application to query-content item pairs not contained in the one or more training sets.

Abstract: The present invention disclosed a method and a system for processing the page information for a mobile communication terminal. The system comprises an access module, an information providing module, a strategy module, a content acquirement module and a page generation module. The information providing module generates the detailed information of a display page according to the access request information, and sends to the strategy module. The strategy module matches the detailed information of the display page with the strategy rule, and obtains a customized rule parameter of the display page, and sends to the content acquirement module. The content acquirement module communicates with the content server of a content service web site, and obtains the page information of the mobile communication terminal. The page generation module generates a browse page according to the customized rule parameter and the page information. The access module sends the browse page to the mobile communication terminal.

Abstract: A method and system for agent redirecting the terminal request is provided. Firstly a uniform service address is preset for each service in the terminals. The terminal initiates a service access request. The agent gateway queries the service subscribing information of the user. The agent gateway determines the type of the service requested by the terminal according to the uniform service address and queries the address of the corresponding service server. The agent gateway transmits the service access request to the service server obtained by query. The service server returns the access response of the service to the terminal via the agent gateway. The present invention can choose the suitable service server and agent redirect the service access request to the designated service server in the case that multiple servers exist by presetting the uniform service address in the terminal.

Abstract: The present invention discloses a method for processing the video telephone calling based on the mobile communication network.