An Ensemble Ranking Solution for the Yahoo! Learning to Rank Challenge
نویسندگان
چکیده
This paper describes our proposed solution for the Yahoo! Learning to Rank challenge. The solution consists of an ensemble of three point-wise, two pair-wise and one list-wise approaches. In our experiments, the point-wise approaches are observed to outperform pairwise and list-wise ones in general, and the final ensemble is capable of further improving the performance over any single approach. In terms of the online validation performance, our proposed solution achieves an ERR of 0.4565 (NDCG 0.7870) for set 1.
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ارائه الگوریتمی مبتنی بر یادگیری جمعی به منظور یادگیری رتبهبندی در بازیابی اطلاعات
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking ...
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