Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data (original) (raw)
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A Neural Framework for Web Ranking Using Combination of Content and Context Features
2009
Containing enormous amounts of various types of data, web has become the main source for finding the desired information. Meanwhile retrieving the desired information in such a vast heterogeneous environment is much difficult. This situation has led to a drastic increase in the popularity of internet search engines. Undoubtedly, designing both efficient and effective ranking strategies as the basic core
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Learning to rank with click-through features in a reinforcement learning framework
International Journal of Web Information Systems, 2016
Purpose Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information. Design/methodology/approach The proposed ranking algorithm (QRC-Rank) applies RL techniques on...
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Context-Aware Learning to Rank with Self-Attention
2020
Learning to rank is a key component of many e-commerce search engines. In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users.Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss. The list is then sorted in the descending order of the scores. Possible interactions between items present in the same list are taken into account in the training phase at the loss level. However, during inference, items are scored individually, and possible interactions between them are not considered. In this paper, we propose a context-aware neural network model that learns item scores by applying a self-attention mechanism. The relevance of a given item is thus determined in the context of all other items present in the list, both in training and in inference. We empirically demonstrate significant perf...
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