Enhancing Search and Recommendation Personalization through User Modeling and Representation
DOI:
https://doi.org/10.22399/ijcesen.3784Keywords:
Dynamic User Modeling, Federated Learning, Transformer Architectures, Personalized Ranking Models, Multimodal User RepresentationAbstract
The capacity to personalize is one of the most important functions in state of the art search and recommendation systems that lead to higher user engagement, satisfaction, and retention and must be discussed as an important feature in today data heavy online worlds. This paper will propose user modeling and representation on three levels namely the technical, methodological, and application level to further personalization in various industries like retail, finance, and real estate. It follows the development towards dynamic, data-augmented pipelines of personalization whose fuel is deep learning, natural language processing (NLP), and large language models (LLMs). At their focus are user modeling which is a systematic representation of abstractions of preferences, behavior patterns, and contextual cues. Upstream approaches covered are matrix factorization, RNN/LSTM sequence modeling, encoder, and transformer-based encoders as well as multimodal embedding models. The paper discusses a challenging issue such as data sparsity and cold-start prediction as well as longest-standing challenges in online learning including context-sensitive ranking algorithms and inference in real-time. It uses a strict data preprocessing pipeline, offline/online A/B testing frameworks and a set of metrics like NDCG, CTR and MAP. Using previous industry experience developing large-scale personalization engines at Amazon and Alibaba, the case study research provides case studies which show how deep learning architectures have revolutionized recommendation effectiveness and business key performance indicators. More upcoming directions even beyond the LLM-powered personalization agents on the one side consist of federated learning, on-device model inference, differential privacy, and continual learning with memory-augmented networks. Ethical necessities: fairness, interpretability, and user control are highlighted so that AI can be properly deployed. Results provide a pragmatic roadmap between theoretical advancement to large scale, privacy conscious and ethical personalization systems that offer appropriately scaled and responsive personalization, achieving the personalization of user experience.
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