Strategic Learning In Recommendation Systems

Effective personalization can help firms reduce their customers’ search costs and enhance customer loyalty. The personalization process consists of two important activities: learning and matching. Learning involves collecting data from a customer’s interactions with the firm and then making inferences from the data about the customer’s profile. Matching requires identifying which products to recommend or links to provide for making a sale. Prior research has typically looked at each activity in isolation. For instance, recent research has studied how a user’s profile can be inferred quickly by offering items (links) that help discriminate user classes. Research on matching has typically assumed that all the recommendations in an interaction are made to generate immediate sales. We examine the problem of identifying items to offer such that both learning and matching are taken into consideration, thereby enabling the firm to achieve higher payoffs in the long run

Optimizing offer sets based on user profiles

Personalization and recommendation systems are being increasingly utilized by ecommerce firms to provide personalized product offerings to visitors at the firms’ web sites. These systems often recommend, at each interaction, multiple items (referred to as an offer set) that might be of interest to a visitor. When making recommendations firms typically attempt to maximize their expected payoffs from the offer set. This paper examines how a firm can maximize its expected payoffs by leverag ing th e kn owledge of the profiles of visitors to their site. We provide a methodology that accounts for the interactions among items in an offer set in order to determine the expected payoff. Identifying the optimal offer set is a difficult problem when the number of candidate items to rec ommend is large. We develop an efficient heuristic for this problem, and show that it performs well for both small and large problem instances.publisher versio

Accelerated learning of user profiles

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation

COMPOSING OFFER SETS TO MAXIMIZE EXPECTED PAYOFFS

Firms are increasingly using clickstream and transactional data to tailor product offerings to visitors at their site. Ecommerce websites have the opportunity, at each interaction, to offer multiple items (referred to as an offer set) that might be of interest to a visitor. We consider a scenario where a firm is interested in maximizing the expected payoff when composing an offer set. We develop a methodology that considers possible future offer sets based on the current choices of the user and identifies an offer set that will maximize expected payoffs for an entire session. Our framework considers both the items viewed and purchased by a visitor and models the probability of an item being viewed and purchased separately when calculating expected payoffs. The possibility of a user backtracking and viewing a previously offered item is also explicitly modelled. We show that identifying the optimal offer set is a difficult problem when the number of candidate items is large and the offer set consists of several items even for short time horizons. We develop an efficient heuristic for the one period look-ahead case and show that even by considering such a short horizon the approach is much superior to alternative benchmark approaches. Proposed methodology demonstrates how the appropriate use of information technologies can help e-commerce sites improve their profitability

Evaluation the relationship of left ventricular global longitudinal strain and laboratory parameters in discharged patients with COVID-19: a follow-up study

Background: The novel coronavirus infection (COVID-19) disease has spread rapidly and posed a great threat to global public health. The laboratory parameters and clinical outcomes of the disease in discharged patients remain unknown. In this study, we aimed to investigate the laboratory and echocardiographic findings of patients with COVID-19 after discharge and the relation between left ventricular global longitudinal strain (LVGLS) and inflammatory parameters in discharged patients. Methods: A total of 75 patients recovering from COVID-19 as the study group were prospectively recruited from the COVID-19 outpatient clinic for their follow-up visits at a median 6 months after discharge. Patients were classified into groups according to pneumonia severity and impairment in LVGLS. Laboratory findings of patients both at admission and after discharge were evaluated and the relation with pneumonia severity at admission and LVGLS after discharge were analyzed. Results: Serum ferritin, lactate dehydrogenase (LDH) and prohormone B-type natriuretic peptide (pro-BNP) levels after discharge were significantly higher in the study group than the control group (n = 44). Ferritin was found to be related to pneumonia severity. Serum ferritin and LDH values after discharge were significantly higher in patients with impaired LVGLS than those with preserved. There was a significant correlation between LVGLS, serum ferritin and LDH values after discharge (r = -0.252, p = 0.012; r = -0.268, p = 0.005, respectively). Conclusions: Clinicians should pay close attention to the serum ferritin and LDH levels in discharged patients for predicting the severity of COVID-19 disease and early identification of subclinical left ventricular myocardial dysfunction

Accelerated Learning of User Profiles

Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation. This paper was accepted by Sandra Slaughter, information systems.personalization, Bayesian learning, information theory, recommendation systems

Impairment of right ventricular longitudinal strain associated with severity of pneumonia in patients recovered from COVID-19.

Myocardial injury caused by COVID-19 was reported in hospitalized patients previously. But the information about cardiac consequences of COVID-19 after recovery is limited. The aim of the study was comprehensive echocardiography assessment of right ventricular (RV) in patients recovered from COVID-19. This is a prospective, single-center study. After recovery from COVID-19, echocardiography was performed in consecutive 79 patients that attended follow-up visits from July 15 to November 30, 2020. According to the recovery at home vs hospital, patients were divided into two groups: home recovery (n = 43) and hospital recovery (n = 36). Comparisons were made with age, sex and risk factor-matched control group (n = 41). In addition to conventional echocardiography parameters, RV global longitudinal strain (RV-GLS) and RV free wall strain (RV-FWS) were determined using 2D speckle-tracking echocardiography (2D STE). Of the 79 patients recovered from COVID-19, 43 (55%) recovered at home, while 36 (45%) required hospitalization. The median follow-up duration was 133 +/- 35 (87-184) days. In patients recovered from hospital, RV-GLS and RV-FWS were impaired compared to control group (RV-GLS: -17.3 +/- 6.8 vs. -20.4 +/- 4.9, respectively [p = 0.042]; RV-FWS: -19.0 +/- 8.2 vs. -23.4 +/- 6.2, respectively [p = 0.022]). In subgroup analysis, RV-FWS was impaired in patients severe pneumonia (n = 11) compared to mild-moderate pneumonia (n = 28), without pneumonia (n = 40) and control groups (-15.8 +/- 7.6 vs. -21.6 +/- 7.6 vs. -20.8 +/- 7.7 vs. -23.4 +/- 6.2, respectively, [p = 0.001 for each]) and RV-GLS was impaired compared to control group (-15.2 +/- 6.9 vs. -20.4 +/- 4; respectively, [p = 0.013]). A significant correlation was detected between serum CRP level at hospital admission and both RV-GLS and RV-FWS (r = 0.285, p = 0.006; r = 0.294, p = 0.004, respectively). Age (OR 0.948, p = 0.010), male gender (OR 0.289, p = 0.009), pneumonia on CT (OR 0.019, p = 0.004), and need of steroid in treatment (OR 17.424, p = 0.038) were identifed as independent predictors of impaired RV-FWS (> -18) via multivariate analysis. We demonstrated subclinic dysfunction of RV by 2D-STE in hospitalized patients in relation to the severity of pneumonia after recovery from COVID-19. 2D-STE supplies additional information above standard measures of RV in this cohort and can be used in the follow-up of these patients

Accelerated Learning of User Profiles

Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation. This paper was accepted by Sandra Slaughter, information systems.personalization, Bayesian learning, information theory, recommendation systems