Enhancing LambdaMART Using Oblivious Trees
Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering. We present experimental results which suggest that the performance of the current state-of-the-art learning to rank algorithm LambdaMART, when used for document retrieval for search engines, can be improved if standard regression trees are replaced by oblivious trees. This paper provides a comparison of both variants and our results demonstrate that the use of oblivious trees can improve the performance by more than $2.2\%$. Additional experimental analysis of the influence of a number of features and of a size of the training set is also provided and confirms the desirability of properties of oblivious decision trees.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringLearning-To-RankQuestion AnsweringregressionRetrievalSimilar Papers 제목 키워드 기반
Factorizing LambdaMART for cold start recommendations
Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the u…
Learning-To-RankMatrix CompletionRecommendation SystemsCache-Friendly Search Trees; or, In Which Everything Beats std::set
While a lot of work in theoretical computer science has gone into optimizing the runtime and space usage of data structures, such work very often neglects a very important component of modern computers: the cache. In doi…
Which Tricks Are Important for Learning to Rank?
Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a decade ago. Recently, several other GBDT…
Learning-To-RankDOFEN: Deep Oblivious Forest ENsemble
Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts. However, DNNs still lag behind Gradient Boosting Decision …
ILMART: Interpretable Ranking with Constrained LambdaMART
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on…
Learning-To-Rank