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Found 5 papers, 0 papers with code
Diversifying by Intent in Recommender Systems
In this work, we show the benefits of incorporating higher-level user understanding, specifically user intents that can persist across multiple interactions or recommendation sessions, for whole-page recommendation toward optimizing long-term user experience.
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
The multi-funnel setup effectively balances between coverage and relevance.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Reward Shaping for User Satisfaction in a REINFORCE Recommender
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?
Recency Dropout for Recurrent Recommender Systems
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
