A Review of Collaborative Filtering Techniques in Startup Marketplace Recommender Systems
摘要
This study is aimed to aid buyers who are unfamiliar with alternative online marketplaces by offering intelligent recommendations that direct them to locate desired goods at reduced rates or with continual reductions. Many shoppers prefer to rely on a few large e-commerce sites, frequently disregarding better prices elsewhere. To address this, the study proposes a trial-based strategy that employs real-world user interaction data from a specific digital media marketplace to undertake a detailed evaluation of consumer behavior and purchasing preferences. By examining data which is browsing history, product categories, and interaction patterns and the system recommends plausible alternative platforms which create comparable or better value. Personalized recommendations are developed by data-driven approaches that match to individual buying habits, enabling customers to make more educated purchases. The method also combines targeted the social media advertising to boost awareness, stimulate app installs, and introduce users to new markets. This imaginative advertising strategy are not only boosts user engagement but also stimulates mobility to other platforms by giving new incentives and rewards. Finally, the purpose is to provide the customers with the greater buy solutions which allow for the rapid price comparisons, amazing savings, and concentrated recommendations and consequently boosting the general internet purchasing experience and extending higher awareness of many marketplace alternatives.