Learning to rank challenge yahoo dating

learning to rank challenge yahoo dating

Winning the transfer learning track of yahoo!'s learning to rank challenge with . An up-to-date comparison of state-of-the-art classification algorithms, Expert. Ranking relevance has been the most critical problem since the birth of web served with up-to-date information, requiring search engines to take freshness. Publication date: Jan Journal title: JMLR: Workshop and Conference Proceedings. Volume: Special issue title: Yahoo! Learning to Rank Challenge.

Peter Cnudde on How Yahoo Uses Hadoop, Deep Learning and Big Data Platform

Can you discuss the algorithms and techniques you are using? We employ a deep convolutional neural network that transforms an input image into a short floating-point vector. CaffeOnSpark has enabled Flickr to train millions of photos on Hadoop clusters, and improve classification accuracy significantly.

learning to rank challenge yahoo dating

The improved accuracy has benefited Flickr users with better image search results. With Esportswe detect game highlights automatically, in real time, from live streamed videos. We are currently using our solution for two applications — automatic tweet generation and match summary generation. In general, detecting highlights from any type of video is very challenging because of the subjective nature in the problem — how do we define a highlight?

Instead of building a system with multiple visual recognizers for detecting visual characteristics like a big splash of lights or turrets in League of Legendsour solution is based on convolutional neural networks, a class of models composed of multiple layers where each layer extracts increasingly high-level information from the previous layer.

Text Classification 5: Learning to Rank

These networks can be trained in an end-to-end fashion with labeled examples: Simply put, we can train a model to learn what are important visual characteristics that define game highlights.

Our solution brings us multiple benefits. First, our system requires no human intervention at runtime because the model detects game highlights from video automatically once trained properly; this allows us to scale up to multiple games and matches day and night.

Second, we can standardize development process for multiple game titles -- the only thing that is different across games is the training dataset, which we annotate with help from domain experts. Can you talk about best practices in implementing a Machine Learning solution in terms of scalability, performance, and security?

Peter Cnudde on How Yahoo Uses Hadoop, Deep Learning and Big Data Platform

Scaling and evolving any platform without sacrificing speed and stability is hard, and everyone should expect challenges. Implementing scalable machine learning algorithms directly on top of Hadoop clusters have made things easier for us in many ways, particularly when it comes to data movement and security. We have also enhanced our Hadoop clusters with high-memory and GPU servers to run parameters servers for large-scale machine learning and deep learning applications respectively.

We make extensive use of YARN features to operate these heterogenous clusters. Networking has also been enhanced with G Infiniband connections between GPU servers in addition to traditional 10G Ethernet that most Hadoop clusters have today for server-to-server direct communication.

The primary purpose of these enhancements is to avoid scale bottlenecks and speed up learning. We also expect to see deep learning carrying machine learning forward.

Deep learning has been highly interesting to academia, and deep learning algorithms are now beating the traditional machine learning algorithms across many benchmarks. Caffe-on-Spark is one such approach we have developed that allows organizations to turn their existing Hadoop or Spark clusters into a powerful platform for deep learning that is fully distributed and supports incremental learning.

What are the challenges your team has encountered in this implementation?

  • LETOR: Learning to Rank for Information Retrieval

This footprint gets close to 45, servers if you include additional 23 multi-tenant HBase and Storm clusters. The task of rank aggregation is to output a better final ranked list by aggregating the multiple input lists. A row in the data indicate a query-document pair.

learning to rank challenge yahoo dating

Several rows are shown as below. In the above example, 2: There are 21 input lists in MQagg dataset and 25 input lists in MQagg dataset.

Learning to rank with extremely randomized trees - Geurts Pierre

Listwise ranking The data format in the setting is very similar to that in supervised ranking. The difference is that the ground truth of this setting is a permutation for a query instead of multiple level relevance judgements. As shown in the following examples, the first column is the relevance degree of a document in ground truth permutation.

Large value of the relevance degree means top position of the document in the permutation. The other columns are the same as that in the setting of supervised ranking.

Due to website update, all the datasets are moved to cloud hosted on OneDrive and can be downloaded here.

Winning The Transfer Learning Track of Yahoo!'s Learning To Rank Challenge with YetiRank

Meta dataMeta data for all queries in the two query sets. The information can be used to reproduce some features like BM25 and LMIR, and can also be used to construct some new features. The first column is the MSRA doc id of the web page, the second column is the number of inlinks of this page, and the following columns list the MSRA doc ids of all the inlinks of this page.

Similarity relation of Gov2 collection The data is organized by queries. Each row in the similarity files describes the similarity between a page and all the other pages under a same query. Here is the an example line: For example, for a query with web pages, the page index ranges from 1 to