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Context-Aware Recommender System with Dynamic Personalization
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AI
DOI：10.1007/978-3-032-05545-3_27.png)
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Abstract
En 中文
This paper explores the implementation and impact of Context-Aware Recommender Systems (CARS) in enhancing user experience on e-commerce platforms. Traditional recommender systems, which rely primarily on past purchase behavior and browsing history, often fall short in delivering timely and contextually relevant recommendations. CARS overcome this constraint by integrating dynamic contextual elements like time of day, place, device type, and user preferences to offer tailored product recommendations. In this study, a CARS was integrated into the H&M e-commerce platform, utilizing a combination of collaborative filtering and context-enhanced algorithms to improve recommendation accuracy and user engagement. According to the results of A/B testing and user feedback, there was a 20% rise in click-through rates (CTR) and a 22% enhancement in conversion rates. This shows that contextually relevant recommendations better match users' current needs and preferences. Furthermore, user satisfaction increased by 85%, with reduced decision fatigue due to the presentation of more relevant, timely products. The study highlights the challenges of obtaining reliable contextual data and balancing privacy concerns with dynamic personalization, but also underscores the potential for CARS to provide e-commerce platforms with a competitive advantage. The paper concludes with suggestions for future research, including scaling the system to handle larger datasets, integrating more granular contextual inputs such as user mood, and exploring advanced interaction methods like augmented reality and voice-enabled recommendations. The findings suggest that CARS can significantly enhance personalization, user satisfaction, and business outcomes, paving the way for more responsive and customer-centric e-commerce experiences.
Keywords:
Context-Aware Recommender Systems (CARS)
E-commerce
Personalization
User Experience
Collaborative Filtering
Real-Time Recommendations
User Satisfaction
Click-Through Rate (CTR)
Conversion Rate
Dynamic Personalization Privacy Concerns
Contextual Data
Adaptive Recommendation Strategies
Matrix Factorization
Deep Learning
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