Multidimensional Next-Generation Time and Transition-Aware Product Recommendation System

Abstract

In the dynamic landscape of e-commerce, the proliferation of products has immensely complicated the process of effective product discovery. With over 14 million items listed on platforms such as Pazarama.com, consumers often struggle to navigate through extensive catalogs to find products that genuinely meet their evolving needs. This challenge is exacerbated in categories requiring sequential consumption, such as baby products, where the progression from one product stage to another is not only inevitable but critical. Traditional recommendation systems primarily rely on static historical data. While these systems provide baseline suggestions based on past purchases or general popularity, they often fail to capture the nuanced and immediate requirements of consumers. For instance, a parent purchasing size one diapers will soon need to transition to size two, and a static system might continue to recommend size one, ignoring the child's growth. Moreover, these systems are not equipped to handle anomalies or data inconsistencies, often stemming from privacy regulations like the General Data Protection Regulation (GDPR), which can skew the effectiveness of the recommendations provided. This paper proposes a novel approach that integrates Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to develop a multidimensional, next-generation product recommendation system. This system accommodates time-sensitive needs and transitions in consumer product stages, predicting future product requirements based on evolving consumer stages while handling anomalies and data inconsistencies due to privacy concerns. Furthermore, it offers real-time updates and integrates seamlessly with social media and online platforms to enhance user engagement and satisfaction. By employing time series analysis and advanced AI techniques, this model aims to improve the accuracy of personalized recommendations, support the introduction and marketing of new or rare products, and ultimately enhance the overall user experience on platforms like Pazarama.com. Through this approach, the paper demonstrates the potential for advanced recommendation systems to transform online retail environments by increasing sales, enhancing customer interaction, and expanding the technological repertoire of e-commerce platforms.