Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: The present disclosure is directed to apparatuses, systems, and methods for automatically managing cost of sale (COS) bidding for merchants (alternatively referred to herein as “vendors”). Described herein are automated COS bidding processes (or automated COS bidding logic, modules, or engines) utilized such that merchants need not actively manage their bids (i.e., fees associated with displaying item listings). As described herein, embodiments automate merchant bids using a formula based, at least in part, on the price of the item, the cost of selling from the advertiser, and historical performance data.

Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: Query suggestions can be generated by identifying desirable sub-queries. Search engine data can be accumulated to determine usage characteristics for various queries. Potential sub-queries can be generated and ranked based on the usage data. After ranking potential sub-queries, the rankings can be used to select sub-queries when a search request is received. The selected sub-queries can be used directly as query suggestions, or the sub-queries can be used as input for another query suggestion engine.

Abstract: The present invention is a computer aided method for guided teaching of language between a teacher and a class of students in a guided teaching session, comprising the steps of: (a) presenting a question to the student at a student display; (b) receiving input from the teacher at the teacher workstation for the teacher to give further information related to the question; (c) presenting information using at least one of a plurality of styles and stimuli corresponding to the input from the teacher on the student display; (d) receiving answer from the teacher at the teacher workstation, wherein the answer is received by the teacher from the student; (e) determining whether the answer is correct; and (f) presenting feedback to the answer at student display. In the guided teaching session, after presenting a question to the students, the teacher may further provide guidance to the students on the student display to assist the students in reaching the correct answer.