Abstract: A method of creating a House of Quality for product design, the House of Quality including a plurality of customer requirements, each of which has corresponding requirement importance includes obtaining a plurality of customer comments on a product; determining, with a processing unit hot words from each of the customer comments, determining at least one customer requirement associated with the each of the comments and assessment rating for that customer requirement based on the hot words; and determining requirement importance of each of the customer requirements.

Abstract: A technical solution for online recommendation. Determining, according to the first user's behaviors in the online decision process, which phase of the online decision process the first user is presented in, wherein the online decision process is divided into a plurality of phases depending on a decision conversion rate; selecting recommended items to be provided to the first user according to one or more second users' historical behavior records, wherein the one or more second users are users who are presented in one or more phases having a higher decision conversion rate than the determined phase.

Abstract: A method and system for generating a search result for a query of hierarchically organized documents based on retrieval of subtrees that are key resources for topic distillation is provided. The retrieval system may identify documents relevant to a query using conventional searching techniques. The retrieval system then calculates a subtree feature for subtrees that have an identified document as their root. After the retrieval system calculates the subtree feature for the subtrees, the retrieval system may generate a subtree relevance score for each subtree based on its subtree feature. The retrieval system may then order the identified documents based on their corresponding subtree relevances.

Abstract: According to a cost-per-action advertising model, advertisers submit ads with cost-per-action bids. Ad auctions are conducted and winning ads are returned with contextually relevant search results. Each time a winning ad is selected by a user, resulting in the user being redirected to a website associated with the advertiser, a selected impression and a price is recorded for the winning ad. Periodically, an advertiser submits a report indicating a number of actions attributed to the ads that have occurred through the advertiser website. The advertiser is then charged a fee for each reported action based on the recorded prices for the winning ads and based on the number of selected impressions recorded for the winning ads.

Abstract: Functionality is described herein for allocating group-buying deals in a group-buying service. In certain implementations, the functionality operates by receiving deal information from deal-providing entities (such as merchants). The deal information describes plural deals. The functionality then assigns a number of impressions to each deal so as to maximize revenue provided to an entity which administers the group-buying service. This yields allocation information. The functionality then presents deals to users in accordance with the allocation information. For example, if the allocated number of impressions for a certain deal is x, then the functionality will provide x opportunities for users to select this deal.

Abstract: The probability that a user clicks on an online advertisement may be dependent on an attractiveness of the online advertisement. In determining such click probability, an advertisement attractiveness model for estimating an attractiveness of an online advertisement to users may be developed. A click behavior model is then created by combining the advertisement attractiveness model with a relevance model. The relevance model may be used for estimating relevance between the online advertisement and a search query. The click behavior model may be applied to features extracted from the online advertisement to calculate a click probability for the online advertisement.

Abstract: A method of monitoring input devices to discover units of work and type of work includes recording uses of input devices of a computer, analyzing the recorded uses against pre-defined use patterns to determine sets of the recorded uses that correspond to one of a plurality of units of work, and outputting an indicator indicating which of the units of work have occurred. A method of accessing a call center includes performing speech to text transcription on audio recordings from the center, determining an identifier identifying an operator for a call from the text, estimating a phase of the call based on the text, recording ant entry including the identifier, the phase, and a time period of the phase, correlating the entry with another entry including information on an application run during the estimated phase to generate a correlated entry, and determining quality level of operator based on correlated entry.

Abstract: A task guidance tool that displays instructional steps and associated advertisements may facilitate the accomplishment of a task by users who are otherwise unfamiliar with the task. The task guidance tool may be developed from input data mined from various sources. The task guidance tool may display a series of step pages in which each step page include instructions for accomplishing a corresponding step of the task. Further, one or more step pages of the task guidance tool may be provided with selected advertisements that are displayed with the step instructions.

Abstract: An advertisement perception predictor may forecast the effectiveness of an online advertisement in a web page by predicting whether the online advertisement may be perceived by a consumer. The advertisement perception predictor may use a perception model that is trained for determining perception probability values of online advertisements. The perception model may be applied to an online advertisement to determine a perception probability value for the online advertisement. The perception probability value may indicate the likelihood that a consumer is likely to view the online advertisement.

Abstract: The relevance of an object, such as a document resulting from a query, may be determined automatically. A graphical model-based technique is applied to determine the relevance of the object. The graphical model may represent relationships between actual and observed labels for the object, based on features of the object. The graphical model may take into account an assumption of noisy training data by modeling the noise.

Abstract: Some implementations generate a mapping function using one or more historic performance indicators for a set of ad-keyword pairs and one or more advertisement metrics extracted from the set of ad-keyword pairs. The mapping function may be applied to map one or more advertisement metrics of a particular ad-keyword pair to determine a quality score for the particular ad-keyword pair. For example, the quality score may be used when determining whether to select an advertisement for display or may be provided as feedback to an advertiser. Additionally, in some implementations, the mapping function may be applied to determine a quality score for a new ad-keyword pair that has not yet accumulated historic information.

Abstract: Some implementations provide techniques for estimating impression numbers. For example, a log of advertisement bidding data may be used to generate and train an impression estimation model. In some implementations, an impression estimation component may use a boost regression technique to determine a predicted impression value range based on a proposed bid received from an advertiser. For example, the predicted impression value range may be determined based on a predicted estimation error. Additionally, in some instances, the predicted impression value range may be evaluated using one or more evaluation metrics.

Abstract: Some implementations construct a quality score table based on historic data collected for a plurality of ad-keyword pairs. An ad-keyword pair may be selected for determining a quality score. One or more advertisement parameters may be determined for the selected ad-keyword pair. Based on the one or more advertisement parameters, the quality score for the selected ad-keyword pair may be determined from the quality score table. In some implementations, the quality score table is constructed by iteratively cutting a directed graph representing the advertisement parameters and the historic data. Further, in some implementations, the table may be smoothed using a smoothing operation.

Abstract: The present invention provides an improved method for ranking documents using a ranking model. One embodiment employs Continuous Conditional Random Fields (CRF) as a model, which is a conditional probability distribution representing a mapping relationship from retrieved documents to their ranking scores. The model can naturally utilize features of the content information of documents as well as the relation information between documents for global ranking. The present invention also provides a learning algorithm for creating Continuous CRF. Also provided, the invention introduces Pseudo Relevance Feedback and Topic Distillation.

Abstract: A “Ranking Optimizer,” provides a framework for directly optimizing conventional information retrieval (IR) measures for use in ranking, search, and recommendation type applications. In general, the Ranking Optimizer first reformats any conventional position based IR measure from a conventional “indexing by position” process to an “indexing by documents” process to create a newly formulated IR measure which contains a position function, and optionally, a truncation function. Both of these functions are non-continuous and non-differentiable. Therefore, the Ranking Optimizer approximates the position function by using a smooth function of ranking scores, and, if used, approximates the optional truncation function with a smooth function of positions of documents. Finally, the Ranking Optimizer optimizes the approximated functions to provide a highly accurate surrogate function for use as a surrogate IR measure.

Abstract: A method and system for determining the contribution of a document within a hierarchy of documents based on the contribution of descendant documents is provided. The contribution system provides a hierarchy of documents that specifies the ancestor/descendant relations between documents. For each document of a hierarchy, the contribution system determines the contribution of each document factoring in the contribution of descendant documents. The contribution may be the relevance of a document to a topic, a feature of a document, and so on.

Abstract: This disclosure describes various exemplary methods, computer program products, and systems for selecting features for ranking in information retrieval. This disclosure describes calculating importance scores for features, measuring similarity scores between two features, selecting features that maximizes total importance scores of the features and minimizes total similarity scores between the features. Also, the disclosure includes selecting features for ranking that solves an optimization problem. Thus, this disclosure identifies relevant features by removing noisy and redundant features and speeds up a process of model training.

Abstract: Procedures for learning and ranking items in a listwise manner are discussed. A listwise methodology may consider a ranked list, of individual items, as a specific permutation of the items being ranked. In implementations, a listwise loss function may be used in ranking items. A listwise loss function may be a metric which reflects the departure or disorder from an exemplary ranking for one or more sample listwise rankings used in learning. In this manner, the loss function may approximate the exemplary ranking for the plurality of items being ranked.

Abstract: A method and system for projecting queries and images into a similarity space where queries are close to their relevant images is provided. A similarity space projection (“SSP”) system learns a query projection function and an image projection function based on training data. The query projection function projects the relevance of the most relevant words of a query into a similarity space and the image projection function projects the relevance to an image of the most relevant words of a query into the same similarity space so that queries and their relevant images are close in the similarity space. The SSP system can then identify images that are relevant to a target query and queries that are relevant to a target image using the projection functions.

Abstract: The present invention provides an improved method for ranking documents using a ranking model. One embodiment employs Continuous Conditional Random Fields (CRF) as a model, which is a conditional probability distribution representing a mapping relationship from retrieved documents to their ranking scores. The model can naturally utilize features of the content information of documents as well as the relation information between documents for global ranking. The present invention also provides a learning algorithm for creating Continuous CRF. Also provided, the invention introduces Pseudo Relevance Feedback and Topic Distillation.