Abstract: A query-processing technique includes an operation of matching the input query against a plurality of candidate target items, to produce a set of candidate query-item pairings. The matching is applicable to different classes of matching, but is performed by a computer processing architecture that uses a class-agnostic instance of query-processing logic and a class-agnostic target item index. After the matching operation, the technique assigns a matching class to each candidate query-item pairing in the set of candidate query-item pairings, to produce a set of classified pairings. The technique ultimately serves a particular output item to an end user, where the particular output item is chosen based on the results of the matching and assigning. Some implementations of the technique include a filtering operation whereby the candidate-item pairings are filtered to conform to a specified selection strategy or strategies. This filtering operation supplements or replaces the assigning operation.

Abstract: A computing system can include an advertisement server that, as part of an auction for a paid search advertisement, calculates an original score for a particular bidder by using a scoring function; calculates an optimized score for the particular bidder by obtaining a score estimate from a system storing results of an offline historical model for the particular bidder, calculating a reserve price value by calculating ?(s*-?i), and adding the reserve price value (which may be a negative value) to the original score; and assigns the particular bidder to a paid search advertisement slot according to the optimized score. The optimized score can be represented by the formula {tilde over (s)}i=si+?(s*??i), where si is the original score, ? is a multiplier between 0 and 1, s* is a fixed point that may be equal to 0, and ?i is the score estimate for the bidder that comes from the offline historical model.

Abstract: Multimedia features extracted from display advertisements may be integrated into a click prediction model for improving click prediction accuracy. Multimedia features may help capture the attractiveness of ads with similar contents or aesthetics. Numerous multimedia features (in addition to user, advertiser and publisher features) may be utilized for the purposes of improving click prediction in ads with limited or no history.

Abstract: The present invention provided techniques that may be used, for example, in online advertising. Techniques are provided that include centralized and aggregated advertisement performance data tracking, which can be used in advertisement selection. Advertisement performance data may be obtained by a central server, such as from many advertisement servers. The central server may aggregate the performance data, and may generate a performance snapshot spanning many advertisements. The snapshot may be used in generating a performance prediction model, which may in turn be used in advertisement selection, or monitoring or tracking associated with advertising. Elements or actions, such as obtaining of performance data, aggregation, generation of the snapshot, and generation of the model, may include frequent or real time updating of such elements or actions.

Abstract: A system for determining predictive models associated with online advertising can include a communications interface, a processor, and a display. The communications interface can be configured to receive a partial dataset. The partial dataset may include user information. The processor can be communicatively coupled to the communications interface and configured to identify the partial dataset. The processor can also be configured to determine a first predictive model corresponding to at least part of the partial dataset and a second predictive model by combining a probability distribution with the first predictive model. The display can be communicatively coupled to the processor and configured to display the second predictive model.

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically).

Abstract: A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically).

Abstract: A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: Techniques are provided for use in online advertisement selection in response to a search query. Techniques are provided in which historical online advertising information is obtained. Segmentation is performed of advertisements and queries and used in generating segment pairs, and an associated advertisement performance is determined for each pair. Segmentation is also performed of a particular query and a candidate advertisement for selection to be served in response, and using the resulting segments, pairs are identified and used in adding to a term set associated with the candidate advertisement, which term set is used in assessing the advertisement for selection.

Abstract: Techniques for improving advertisement relevance for sponsored search advertising. The method includes steps for processing a click history data structure containing at least a plurality of query-advertisement pairs, populating a first translation table containing a co-occurrence count field, populating a second translation table containing an expected clicks field, and calculating a click propensity score for an advertisement using the click history data structure, the first translation table (for determining overall click likelihood across all historical traffic), and using the second translation table (for removing biases present in the first translation table).

Abstract: The present invention is directed towards systems and method for organization of bookmarks. The method according to one embodiment comprises retrieving one or more bookmarks associated with one or more content items, a given bookmark generated by a user of a client device and identifying one or more tags associated with one or uniform resource locators corresponding to the or more bookmarks. A bookmark folder hierarchy is created through use of a clustering algorithm on the basis of the one or more tags associated with the one or more uniform resource locators corresponding to the one or more bookmarks.

Abstract: An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

Abstract: An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

Abstract: Search and advertising systems may be optimized through the use of user feedback. Selected parameters such as ranking, filtering, placement, and pricing may be optimized to achieve certain objectives. The optimization may include real-time user monitoring of multiple configurations with various parameters. In one embodiment, a subset of user queries may be assigned to a particular configuration for monitoring and measuring the real-time performance of that configuration. The performance for multiple configurations may be used to identify optimal settings.

Abstract: Methods and systems are provided for predicting click through rate in connection with a particular user, keyword-based query, and advertisement using a probabilistic latent variable model. Click through rate may be predicted based on historical sponsored search activity information. Predicted click through rate may be used as a factor in determining advertisement rank.

Abstract: The present invention is directed towards systems and method for organization of bookmarks. The method according to one embodiment comprises retrieving one or more bookmarks associated with one or more content items, a given bookmark generated by a user of a client device and identifying one or more tags associated with one or uniform resource locators corresponding to the or more bookmarks. A bookmark folder hierarchy is created through use of a clustering algorithm on the basis of the one or more tags associated with the one or more uniform resource locators corresponding to the one or more bookmarks.

Abstract: A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.