Rank-Order Correlation-Based Feature Vector Context Transformation for Learning to Rank for Information Retrieval

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Efficient Margin-Based Rank Learning Algorithms for Information Retrieval

Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods available, most of them need to explicitly model the relations between every pair of relevant and irrelevant documents, and thus result in an expensive training process for large collections. The goal of this paper is to pr...

متن کامل

Learning to Rank Using High-Order Information

The problem of ranking a set of visual samples according to their relevance to a query plays an important role in computer vision. The traditional approach for ranking is to train a binary classifier such as a support vector machine (svm). Binary classifiers suffer from two main deficiencies: (i) they do not optimize a ranking-based loss function, for example, the average precision (ap) loss; a...

متن کامل

Two-Stage Learning to Rank for Information Retrieval

Current learning to rank approaches commonly focus on learning the best possible ranking function given a small fixed set of documents. This document set is often retrieved from the collection using a simple unsupervised bag-of-words method, e.g. BM25. This can potentially lead to learning a sub-optimal ranking, since many relevant documents may be excluded from the initially retrieved set. In ...

متن کامل

Learning to Rank for Information Retrieval Using Genetic Programming

One central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to automatically generate an effective ranki...

متن کامل

Performance Comparison of Learning to Rank Algorithms for Information Retrieval

Learning to rank is the problem of ranking objects by using machine learning techniques. One of the applications of learning to rank is for ranking document of search results. In this research, we compare the performance of three learning to rank algorithms: RankSVM, LambdaMART, and Additive Groves. RankSVM, which is ranking variant of the classical SVM algorithm, is commonly used as a baseline...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Computer Systems Science and Engineering

سال: 2018

ISSN: 0267-6192

DOI: 10.32604/csse.2018.33.041