Abstract: The present technology is directed to high performing trained machine learning models for information retrieval in a web store. In some embodiments, for example, when a search query is received from a user of an online retailer, a computer system associated with the online retailer accesses measurements of performance of sets of search results returned in response to previous user search queries. Each of the previous search results set is a set that was ranked by a machine learning model selected from a store of machine learning models that are each trained to rank search results. Based on the measurements of performance, the computer system selects a machine learning model to rank search results for a response to the received search query. The ranked search results are provided for output to the user.

Abstract: This present invention discloses a bifunctional adsorptive material capable of adsorbing both cations and anions in aqueous phase, obtainable by synthesizing aluminum ion doped SBA-15 molecular sieves from P123 triblock copolymers, tetraethoxysilane, and aluminum isopropoxide to obtain multiple cationic active adsorption sites, and by grafting large sterically hindered organic groups onto the surface of Al-SBA-15 to obtain multiple anionic active adsorption sites. This kind of adsorptive material has two types of adsorption sites for ions of opposite charges. The large sterically hindered organic groups prevent spontaneous recombination reaction between the two types of adsorption sites, enabling the adsorptive material to have excellent adsorption capacity for wastewater treatment involving both cations and anions.

Abstract: Risk-controlled operations cost performance modeling and associated systems and methods are disclosed herein. A retail store generates operations data and performance data, where the operations data represents values of an operations parameter collected over a period of time from the retail store and the performance data represents a value of a performance parameter measured for each of the values of the operations data. Based on the operations and performance data, an initial relationship model is generated. A confidence interval for the initial relationship model is generated using intermediate relationship models, generated by subsampling the operations and performance data. The confidence interval is used to select an operations threshold, which modifies the initial relationship model to generate a risk-controlled relationship model. The risk-controlled relationship model is used to select a value of the operations parameter for use in the retail environment to achieve a desired performance value.

Abstract: This present invention discloses a bifunctional adsorptive material capable of adsorbing both cations and anions in aqueous phase, obtainable by synthesizing aluminum ion doped SBA-15 molecular sieves from P123 triblock copolymers, tetraethoxysilane, and aluminum isopropoxide to obtain multiple cationic active adsorption sites, and by grafting large sterically hindered organic groups onto the surface of Al-SBA-15 to obtain multiple anionic active adsorption sites. This kind of adsorptive material has two types of adsorption sites for ions of opposite charges. The large sterically hindered organic groups prevent spontaneous recombination reaction between the two types of adsorption sites, enabling the adsorptive material to have excellent adsorption capacity for wastewater treatment involving both cations and anions.

Abstract: The present technology is directed to high performing trained machine learning models for information retrieval in a web store. In some embodiments, for example, when a search query is received from a user of an online retailer, a computer system associated with the online retailer accesses measurements of performance of sets of search results returned in response to previous user search queries. Each of the previous search results set is a set that was ranked by a machine learning model selected from a store of machine learning models that are each trained to rank search results. Based on the measurements of performance, the computer system selects a machine learning model to rank search results for a response to the received search query. The ranked search results are provided for output to the user.

Abstract: A recommendation system and method access a recommendation bundle pool including multiple recommendation algorithms, each of which is capable of generating one or more recommendations. A first recommendation bundle comprising two or more recommendation algorithms is selected from the pool. Using the first recommendation bundle, recommendations are generated to provide to visitors to a website. When the recommendation system detects a triggering condition for a scaling cycle, the recommendation system applies a scaling mechanism to increase an exploration of additional recommendation bundles from the recommendation bundle pool. Based on the exploration, the recommendation system selects a second recommendation bundle including algorithms selected from the pool. Recommendations are generated using the second recommendation bundle.