A Statistical Real-Time Prediction Model for Recommender System

TL;DR

This paper presents a statistical real-time prediction model for recommender systems that leverages user activities and product data, achieving promising accuracy in predicting user buying behavior during online shopping sessions.

Contribution

It introduces a novel statistical model for real-time user behavior prediction in recommender systems, utilizing user activity and product information.

Findings

Achieved approximately 58% true-positive rate

Reduced false-positive rate to about 13%

Validated on RecSys Challenge 2015 dataset

Abstract

Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites. An effective and efficient recommender system benefits both the seller and the buyer significantly. We considered user activities and product information for the filtering process in our proposed recommender system. Our model has achieved inspiring result (approximately 58% true-positive and 13% false-positive) for the data set provided by RecSys Challenge 2015. This paper aims to describe a statistical model that will help to predict the buying behavior of a user in real-time during a session.