Long Term User

Long-term user engagement is a crucial objective in recommender systems, shifting the focus from immediate user responses to sustained satisfaction and retention. Current research emphasizes modeling user behavior over extended periods, employing reinforcement learning and Markov Decision Processes to optimize for metrics like daily active users and overall user enjoyment. This involves developing sophisticated algorithms that consider diverse user intents and balance short-term engagement with long-term user experience, leading to improved system design and more effective personalized recommendations across various platforms.