Abstract: A system that uses power spectrum analysis and auto-correlation function analysis to perform seasonality estimation of time series data. A power spectrum analyzer calculates and analyzes a power spectrum of a received time series data. An auto-correlation function analyzer calculates at least one auto-correlation function of the received time series, and generates a resulting set of one or more candidate seasonalities. A seasonality estimator estimates one or more seasonalities of the received time series using at least a portion of the analyzed result from the power spectrum analyzer and using the set of one or more candidates generated by the auto-correlation function analyzer. Accordingly, the estimation of candidate seasonality uses both auto-correlation and power spectrum analysis, thereby at least in some circumstances improving the seasonality estimation compared to auto-correlation function analysis alone or power spectrum analysis alone.

Abstract: A system and method for automatically classifying ads into a taxonomy of categories, the method including: extracting text features from ad images using OCR (optical character recognition) techniques; identifying objects of interest from ad images using object detection and recognition techniques in computer vision; extracting text features from the web-page of the advertiser to which the user is re-directed when clicking the ad; training statistical models using the extracted features mentioned above as well as advertiser attributes from a historical dataset of ads labeled by human editors; and determining the relevant categories of unlabeled ads using the trained models.

Abstract: Refining a target audience for an advertising campaign, includes: obtaining a seed list of customers; defining the target audience as the customers from the seed list who share key characteristics of a desired customer; formulating an audience model; using the audience model, generating a client-specific segment of the defined target audience for targeting; and optimizing the client-specific segment using conversion data.

Abstract: A system for incentivizing sharing advertisements (“ads”) and associated deals with others includes a processor programmed to transmit to a user, for display in an application window of a communication device of a user, an advertisement and an associated deal with an economic incentive for sharing the advertisement with first persons in a social network of the user. The system tracks and stores referral activity by the first persons in the social network of the user in relation to the advertisement, the referral activity including the first persons sharing the advertisement with second persons. The system tracks and stores conversion activity such as purchasing by the first persons in the social network of the user in relation to the deal and purchasing by second persons referred by the first persons.

Abstract: A system and method for automatically classifying ads into a taxonomy of categories, the method including: extracting text features from ad images using OCR (optical character recognition) techniques; identifying objects of interest from ad images using object detection and recognition techniques in computer vision; extracting text features from the web-page of the advertiser to which the user is re-directed when clicking the ad; training statistical models using the extracted features mentioned above as well as advertiser attributes from a historical dataset of ads labeled by human editors; and determining the relevant categories of unlabeled ads using the trained models.

Abstract: A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.

Abstract: The likelihood that a query belongs to a vertical is determined. The query is assigned to a vertical based on that likelihood. A query submitted to a main search box is assigned to verticals and processed using indices specific to those verticals. The query is assigned to verticals based on coverage adjusted log likelihood per unit (CALL) values for that query in those verticals. An offline learning component computes UNITS dictionaries and learns the distributions of query units in the main search and each of the vertical searches using query logs. An online scoring and ranking component uses the query distributions and UNITS dictionaries to determine the CALL values and a likelihood of the query belonging to any vertical. The search query is then assigned to verticals based on the likelihoods. The search query is then processed by all the verticals the query is assigned to.

Abstract: The likelihood that a query belongs to a vertical is determined. The query is assigned to a vertical based on that likelihood. A query submitted to a main search box is assigned to verticals and processed using indices specific to those verticals. The query is assigned to verticals based on coverage adjusted log likelihood per unit (CALL) values for that query in those verticals. An offline learning component computes UNITS dictionaries and learns the distributions of query units in the main search and each of the vertical searches using query logs. An online scoring and ranking component uses the query distributions and UNITS dictionaries to determine the CALL values and a likelihood of the query belonging to any vertical. The search query is then assigned to verticals based on the likelihoods. The search query is then processed by all the verticals the query is assigned to.

Abstract: A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.