![]() A query-document pair is represented by a 46-dimensional feature vector. The larger the relevance label, the more relevant the query-document pair. The first column is relevance label of this pair, the second column is query id, the following columns are features, and the end of the row is comment about the pair, including id of the document. You can download OHSUMED, LETOR MQ2007, LETOR MQ2008 from this web page.Įach row is a query-document pair. Execution Instructions RequirementsĪpache Ant 1.8+ (Not required to run, however the following instructions will assume Apache Ant has been installed.) The implemented algorithms include pointwise, pairwise, and listwise approaches. ![]() This project currently only implements algorithms of the supervised learning variety, and thus we will focus on supervised learning in this document. Thus, during learning, information about all of the documents is necessary. While the model may output a score for one document, the loss function requires a full list of documents in a query. The listwise approach involves looking at lists of documents associated with a query. ![]() Depending on the algorithm, the model may output a score for a single document, however the loss function (and thus training) will usually require a pair of documents in this case. For example, the model may make a prediction about which document in a pair is more relevant, and compare that to the labels of each document. Learning occurs by looking at which document is more relevant. The pairwise approach involves considering documents in pairs. In addition, this method does not take into account the fact that documents may actually be related, or that documents are related to a certain query. Because of this, the final rank of the document is not visible to the loss function. Each document will have a relevance score, which is unrelated or unaffected by other documents. In pointwise, training involves looking at the data or documents independently. In Learning to Rank, there are three different approaches: pointwise, pairwise, and listwise. There are three different types of machine learning algorithms: supervised learning (labels, or the "correct answer," is provided for all data), unsupervised learning (no labels provided), and reinforcement learning (training through trial and error in a specific environment). Learning to Rank is the application of machine learning to construct the ranking model. The ability for a "ranker" to rank the search results accurately is dependent on the ranking model. If all of the user's desired results are pushed to the top of the list, then it does not take much effort for the user to find their desired results. Ranking serves as means of mitigating this problem. The problem with this, however, is that it becomes much harder for the user to find the desired results among the large pool of search results. Thus, by vastly increasing the number of search results, one can hope to include all of the desired results of the user. It is largely impossible to have a pool of search results which will exactly match the set of results the user is looking for. Ranking models are an essential component of information retrieval systems.Ĭonsider the pool of search results for a given search. The general overview is meant to provide a "big picture," interpretation, or explanation of each algorithm, as well as pseudo code.įor specific mathematical details and formulation, please see implementation or original articles. The purpose of this document is to provide instructions on how to execute the program, some experimental results.Ī general overview is also provided at the end of the document for those who would like to review or learn more about the algorithms. Learning-to-Rank for Apache Lucene (compatibility with Apache Lucene is still a work-in-progress).
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