Recommendation Systems Using Event-Based Temporal Data Model

Abstract

Despite challenges like concept drifts, or temporal dynamics in RS, RS has grown in popularity due to its usefulness in meeting customers' needs by helping them find things they might like based on past purchases and interests. Despite their great effectiveness in generating recommendations, conventional RS techniques fall short when it comes to providing accurate ideas due to problems with concept drift. The development of temporal models to account for concept drifts and guarantee more accurate recommendations has been the focus of a lot of research in the wake of these issues, giving rise to dynamic recommender systems (DRSs). However, the bulk of the effort needed to address the drift of interest is put in developing long-term and short-term models for users. It is not possible to dynamically track users' changing interests. We express doubts in this position paper about the practice of computing evaluation metrics for recommender systems as single numbers since these values only represent average effectiveness over a very long time period (for example, a year or longer). This approach only provides a vague, unchanging picture of the facts. To better understand the performance of recommender systems, we propose that researchers compute metrics across time series such as weeks or months and present the results visually, using a line chart, for instance. Insightful forecasts about an algorithm's future performance can be made with the use of results that show how an algorithm's effectiveness evolves over time. As a result, we'll be able to make more informed selections about which algorithms to utilize in a recommender system by collecting more data on an algorithm's performance over time, spotting trends, and developing more accurate forecasts about an algorithm's future performance.