Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface—will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics, such as Click-through rates, Buy-through rates, and Conversion revenue

Statistical shape theory for activity modeling

Monitoring activities in a certain region from video data is an important surveillance problem today. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. In this paper we propose an approach using statistical shape theory (based on Kendall’s shape model) [3]. In a low resolution video each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learnt for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model is below a certain threshold. The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behaviors. 1

Statistical Shape Theory for Activity Modeling

surveillance problem today. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. In this paper we propose an approach using statistical shape theory (based on Kendall's shape model) [3]. In a low resolution video each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learnt for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model is below a certain threshold. The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behaviors

Erratum to: Optical and Magnetic Properties of Free-standing Silicene, Germanene and T-graphene System

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