Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences beten complete result rankings. Both types of approaches have shown promise, but they have not previously been compared to each other. It is therefore unclear which type of approach is the most suitable for which online learning to rank problems. In this work  present the first empirical comparison of absolute and relative online learning to rank approaches.
['Yiwei Chen', 'Katja Hofmann']
I am writing a research paper on a idea Online Learning to Rank: Absolute vs. I am writing a research paper on a idea Relative
[{"from": "human", "value": "I am writing a research paper on a idea Online Learning to Rank: Absolute vs. I am writing a research paper on a idea Relative"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences beten complete result rankings. Both types of approaches have shown promise, but they have not previously been compared to each other. It is therefore unclear which type of approach is the most suitable for which online learning to rank problems. In this work  present the first empirical comparison of absolute and relative online learning to rank approaches."}]