Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing rank...Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.展开更多
What is a real time agent,how does it remedy ongoing daily frustrations for users,and how does it improve the retrieval performance in World Wide Web?These are the main question we focus on this manuscript.In many dis...What is a real time agent,how does it remedy ongoing daily frustrations for users,and how does it improve the retrieval performance in World Wide Web?These are the main question we focus on this manuscript.In many distributed information retrieval systems,information in agents should be ranked based on a combination of multiple criteria.Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness.Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined.The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination.Different ways for ranking agents make this strategy difficult.In this research,we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time.The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes.The proposed approach was tested on the queries provided by Text Retrieval Conference(TREC).Experimental results showed that our approach is effective and robust compared with offline search platforms.展开更多
文摘Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.
基金This research was developed at the University of Ottawa as part of“SAMA”search enginea.
文摘What is a real time agent,how does it remedy ongoing daily frustrations for users,and how does it improve the retrieval performance in World Wide Web?These are the main question we focus on this manuscript.In many distributed information retrieval systems,information in agents should be ranked based on a combination of multiple criteria.Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness.Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined.The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination.Different ways for ranking agents make this strategy difficult.In this research,we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time.The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes.The proposed approach was tested on the queries provided by Text Retrieval Conference(TREC).Experimental results showed that our approach is effective and robust compared with offline search platforms.
