24 research outputs found

    Surveys without Questions: A Reinforcement Learning Approach

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    The 'old world' instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers' online interactions; failing to capture a customer's interactions over time since the rating is a one-time snapshot; and inability to tie back customers' ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer's online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

    Economics, Psychology, and Social Dynamics of Consumer Bidding in Auctions

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    With increasing numbers of consumers in auction marketplaces, we highlight some recent approaches that bring additional economic, social, and psychological factors to bear on existing economic theory to better understand and explain consumers' behavior in auctions. We also highlight specific research streams that could contribute towards enriching existing economic models of bidding behavior in emerging market mechanisms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47034/1/11002_2005_Article_5901.pd

    Combining Buy-In Penalties with Commissions at Auction Houses

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    Most auction sellers consign property to auction houses rather than holding the auction themselves. In addition to charging sellers a commission on property that sells in the auction, many auction houses also specify buy-in penalties in auction contracts. This is an amount the seller must pay the auction house if the property fails to sell at auction. An important managerial question for auction houses is whether and when buy-in penalties can increase revenues of the auction house, seller, or both, and what combinations of commission and buy-in penalty to use. We show that auctions which combine buy-in penalties with lower commissions Pareto-dominate auctions that use only commissions. This strategy motivates the seller to set a lower reserve, which creates a surplus in auction revenues that can go to one or both parties. This strategy is Pareto-dominant even if the auction house and the seller are uncertain about the number of bidders at the auction, or the auction house is uncertain about the seller's own valuation for the property, at the time the buy-in penalty, commission, and reserve are contractually set. We also discuss the incentive issues raised by this strategy.auctions, bidding, commissions, pricing, penalty

    Dextran-Gated, Multifunctional Mesoporous Nanoparticle for Glucose-Responsive and Targeted Drug Delivery

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    Design of drug delivery nanocarrier having targeted recognition followed by bioresponsive controlled release, especially via glucose-responsive release, is a challenging issue. Here, we report magnetic mesoporous silica (MMS)-based drug delivery nanocarrier that can target specific cell and release drug via glucose-responsive gate. The design involves synthesis of MMS functionalized with phenylboronic acid and folate. After drug loading inside the pores of MMS, outside of the pores are closed by dextran via binding with phenylboronic acid. Dextran-gated pores are opened for drug release in the presence of glucose that competes binding with phenylboronic acid. We found that tolbutamide and camptothecin loaded MMS can target beta cells and cancer cells, respectively, release drugs depending on bulk glucose concentration and offers glucose concentration dependent cytotoxicity. Developed functional MMS can be used for advanced drug delivery applications for diabetes and cancers with more efficient therapy

    β‑Cyclodextrin Functionalized Magnetic Mesoporous Silica Colloid for Cholesterol Separation

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    Although cholesterol plays significant biochemical function in the human body, excess of it leads to various disorders, and thus, its control/separation is important in medical science and food industries. However, efficient and selective separation of cholesterol is challenging because cholesterol often exists in microheterogeneous or insoluble forms in remote organ and exists with other chemicals/biochemicals. Here, we have described a colloidal magnetic mesoporous silica (MMS)-based approach for efficient separation of cholesterol in different forms. MMS is functionalized with β-cyclodextrin for selective binding with cholesterol via host–guest interaction. The colloidal form of MMS offers effective interaction with cholesterol of any form, and magnetic property of MMS offers easier separation of bound cholesterol. Functionalized MMS is efficient in separating cholesterol crystals, water-insoluble cholesterol, and the microheterogeneous form of cholesterol from milk or a cellular environment. Developed material can be used to remove cholesterol from a complex bioenvironment and extended for large-scale cholesterol separation from food
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