A novel ranking method named as QueryFind, based on learning from historical query logs, is proposed to predict users' information needs and reduce the see... PH Wang,JY Wang,HM Lee - IEEE International Conference on E-technology, E-commerce & E-service ...
Weakly supervised learningActor–action semantic segmentationMulti-task rankingModeling human behaviors and activity patterns has attracted significant research interest in recent years. In order to accurately model human behaviors, we need to perform fine-grained human activity understanding in videos. Fine...
applications including recommendation systems, transductive / multi-label active learning, active learning in regression and active feature acquisition among others... S Chakraborty,J Zhou,V Balasubramanian,... - IEEE International Conference on Data Mining 被引量: 19发表: 2013年 Supervised Collaborative...
These new properties make Fidelity more feasible for defining an optimal ranking function in information retrieval. To learn the underlying ranking function, we further propose a generalized additive model to minimize this Fidelity loss. As a result, our new learning method named Fidelity Rank (FRank...
Active Learning of Label Ranking Functions The effort necessary to construct labeled sets of examples in a supervised learning scenario is often disregarded, though in many applications, it is a tim... K Brinker - International Conference on Machine Learning 被引量: 91发表: 2004年 Active Sampling...
In this chapter, we introduce semi-supervised learning for ranking. The motivation of this topic comes from the fact that we can always collect a large number of unlabeled documents or queries at a low cost. It would be very helpful if one can leverage such unlabeled data in the learning-...
Learning to Rank ranking is learned according to examples via supervised or semi-supervised methods Definition Ranking objects in a network may refer to sorting the objects according to importance, popularity, influence, authority, relevance, similarity, and proximity, by utilizing link information in ...
Weakly supervised learning:弱监督学习指的是使用诸如BM25的现有检索模型自动生成查询文档标签的学习策略。该学习策略不需要带标签的训练数据。除了 ranking 之外,弱监督已经在其他信息检索任务中显示出成功的结果,eg,query performance prediction 、learning relevance-based ...
supervised contrastive learning for the document ranking problem. We perform data augmentation by creating training data using parts of the relevant documents in the query-document pairs. We then use a supervised contrastive learning objective to learn an effective ranking model from the augmented ...
An iterative co-ranking algorithm,which aimed to extend learning to rank from a supervised setting into a semi-supervised setting,was proposed.The approach employed two listwise rankers to identify document permutations for an unlabeled query.In particular,the use of likelihood listwise loss was introd...