learning to rank

Here are the ins and outs of both. SUMMARY Learning to rank refers to machine learning techniques for training the model in a ranking task. We just need to train the model on the order, or ranking of the documents within that result set. To perform learning to rank you need access to training data, user behaviors, user profiles, and a powerful search engine such as SOLR.. Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, as a human user, if those better documents aren’t first in the list, they aren’t very helpful. Figure 4 – Relevance in flight search: a search result is relevant if you bought it. And there is. Can these advances can reasonably be used to enhance our applications, right now? Data scientists create this training data by examining results and deciding to include or exclude each result from the data set. This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance. PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank. The diagram below shows Wayfair’s search system. Spaceships and science fiction cool. Suppose to be in a learning to rank scenario. It turns out, constructing an accurate set of training data is not easy either, and for many real-world applications, constructing the training data is prohibitively expensive, even with improved algorithms. Liu first gives a comprehensive review of the major approaches to learning to rank. As a case study, we chose to do experiments on the real-world service named Sobazaar. Wayfair is a public e-commerce company that sells home goods. Done well, you have happy employees and customers; done poorly, at best you have frustrations, and worse, they will never return. Our first two submissions … How NLP and Deep Learning Make Question Answering Systems Work. You need to decide on the approach you want to take before you begin building your models. Intensive studies have been conducted on the problem and significant progress has been made[1],[2]. Regression means giving similar documents a similar function value, so that we can assign them similar preferences during the ranking procedure. I n 2005, Chris Burges et. This plugin powers search at places like Wikimedia Foundation and Snagajob. al. Relevancy engineering is the process of identifying the most important features of document set to the users of those documents, and using those features to tune the search engine to return the best fit documents to each user on each search. The results show that this model has improved Wayfair’s conversion rate of customer queries. All make use of pairwise ranking. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. [9] proposed ListRank-MF, a list-wise probabilistic MF method that optimizes the cross entropy between the distribution of the observed and predicted ratings. Minimum requirements. The most common implementation is as a re-ranking function. Learning to Rank applies machine learning to relevance ranking. In other words, each tree contributes to a gradient step in the direction that minimizes the loss function. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Like earlier many machine learning processes, we needed more data, and we were using only a handful of features to rank on, including term frequency, inverse document frequency, and document length. Learning to rank, in parallel with learning for classifica-tion and regression, has been attracting increasing interests in statistical learning for the last decade, because many ap-plications such as web search and retrieval can be formalized as ranking problems. Even with careful crafting, text tokens are an imperfect representation of the nuances in content. RankNet is a pairwise approach and uses the GD to update the model parameters in order to minimize the cost (RankNet was presented with the Cross-Entropy cost function). 79 percent of people who don’t like what they find will jump ship and search for another site. The ranking task is the task of ・］ding a sort on a set, and as such is related to the task of learning structured outputs. We’re also always on the hunt for collaborators or for more folks to beat up our work in real production systems. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. RankNet introduces the use of the Gradient Descent (GD) to learn the learning function (update the weights or model parameters) for a LTR problem. Wayfair’s then trains its LTR model on clickstream data and search logs to predict the score for each product. The Elasticsearch Learning to Rank plugin (Elasticsearch LTR) gives you tools to train and use ranking models in Elasticsearch. In other words, it’s what orders query results. Finding just the right thing when shopping can be exhausting. Considerations: What technical and non-technical considerations come into play with Learning to Rank. There has been a lot of attention around machine learning and artificial intelligence lately. As data sets continue to grow, so will the accuracy of LTR. We compare this higher-lower pair against the ground truth (the gold standard of hand ranked data that we discussed earlier) and adjust the ranking if it doesn’t match. Website Terms & Conditions Privacy Policy   Cookie Policy © 2021 OpenSource Connections, LLC, We value your privacy. The search engine then looks up the tokens from the query in the inverted index, ranks the matching documents, retrieves the text associated with those documents, and returns the ranked results to the user as shown below. What considerations play in selecting a model? Incorporating additional features would surely improve the ranking of results for relevant search. The hope is that such sophisticated models can make more nuanced ranking decisions than standard ranking functions like TF-IDF or BM25. Because the training model requires each feature be a numerical aspect of either the document or the relationship of the document to the user, it must be re-computed each time. Models: What are the prevalent models? Next, they use a variety of NLP techniques to extract entities, analyze sentiments, and transform data. Pointwise approaches look at a single document at a time using classification or regression to discover the best ranking for individual results. Skyscanner, a travel app where users search for flights and book an ideal trip uses LTR for. Learn how the machine learning method, learning to rank, helps you serve up results that are not only relevant but that are ranked by relevancy. Under the hood, they have trained a LTR model (used by Solr) to assign a relevance score to the individual products returned for the incoming query. These models exploit the Gradient Boosted Trees that is a cascade of trees, in which the gradients are computed after each new tree, to estimate the direction that minimizes the loss function (that will be scaled by the contribution of the next tree). articles by the same publisher, tracks by the same artist). Microsoft Develops Learning to Rank Algorithms, RankNet, LambdaRank, and LambdaMART are popular learning to rank algorithms developed by, Learning to Rank Applications in Industry, , Wayfair talks about how they used learning to rank for the purpose of keyword searches. The Search, Learning, and Intelligence team at Slack also used LTR to improve the quality of Slack’s search results. As a relevancy engineer, we can construct a signal to guess whether users mean the adjective or noun when searching for ‘dress’. In building a model to determine these weights, the first task was to build a labeled training set. In this technique, we train another machine learning model used by Solr to assign a score to individual products. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence machine learning explores the study and construction of algorithms that can learn from and make predictions on data. As an engineer, artificial intelligence (AI) is cool. Introduction to RankNet. This approach has been incorporated to Slack’s top results module, which shows a significant increase in search sessions per user, an increase in clicks per search, and a reduction in searches per session. Learning to rank ties machine learning into the search engine, and it is neither magic nor fiction. 235 Montgomery St. Suite 500 Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. They extract text information from different datasets including user reviews, product catalog, and clickstream. At search time, individual queries are also parsed into tokens. Skyscanner’s goal is to help users find the best flights for their circumstances. Both building and evaluating models can be computationally expensive. Whole books and PhDs have been written on solving it. In information retrieval systems, Learning to Rank is used to re-rank the top N retrieved documents using trained machine learning models. Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch - dremovd/elasticsearch-learning-to-rank 31 Aug 2020 • wildltr/ptranking • In this work, we propose PT-Ranking, an open-source project based on PyTorch for developing and evaluating learning-to-rank methods using deep neural networks as the basis to construct a scoring function. Relevant search relaxes the age constraint and takes into account how well the document matches the query terms. So people tuned by hand, and iterated over and over. This indicates that Slack users are able to find what they are looking for faster. Learning-to-rank from implicit feedback Introduction. Wayfair then feeds the results into its in-house Query Intent Engine to identify customer intent on a large portion of incoming queries and to send many users directly to the right page with filtered results. Note here that search inside of Slack is very different from typical web search, because each Slack user has access to a unique set of documents and what’s relevant at the time frequently changes. Applications: Using learning to rank for search, recommendation systems, personalization and beyond. Regression is … We add those up and sort the result list. The results indicate that the LTR model with machine learning leads to better conversion rates – how often users would purchase a flight that was recommended by Skyscanner’s model. We use cookies to help give you the best experience on our site and to understand how you interact with our site, Pete learns how to scale up search result rating, A call for a truly open Elasticsearch community, Migrate to Solr or Elasticsearch with this Playbook. So give it a go and send us feedback! Learning to Rank (LTR) applies machine learning to search relevance ranking. Thanks to the widespread adoption of m a chine learning it is now easier than ever to build and deploy models that automatically learn what your users like and rank your product catalog accordingly. We never send a trainer to just “read off slides”. Watch for more articles in coming weeks on: If you think you’d like to discuss how your search application can benefit from learning to rank, please get in touch. Learning to Rank is an open-source Elasticsearch plugin that lets you use machine learning and behavioral data to tune the relevance of documents. How much of this is still cool and fiction? learning to rank or machine-learned ranking (MLR) applies machine learning to construct of ranking models for information retrieval systems. In other words, it’s what orders query results. LambdaRank is based on the idea that we can use the same direction (gradient estimated from the candidates pair, defined as lambda) for the swapping, but scaling it by the change of the final metric, such as nDCG, at each step (e.g., swapping the pair and immediately computing the nDCG delta). One of the cool things about LightGBM is that it … We expect you to bring your hardest questions to our trainers. Learning to Rank using Gradient Descent ments returned by another, simple ranker. Since users expect search results to return in seconds or milliseconds, re-ranking 1000 to 2000 documents at a time is less expensive than re-ranking tens of thousands or even millions of documents for each search. We have to manage a book catalog in an e-commerce website. The three major approaches to LTR are known as pointwise, pairwise, and listwise. Learning to rank is a machine learning method that helps you serve up results that are not only relevant but are … wait for it … ranked by that relevancy. Today, we have larger training sets and better machine learning capabilities. Include the required contrib JARs. Slack provides two strategies for searching: recent and relevant. Previously unseen documents to be ranked for queries seen in the training set. Learning to rank (LTR) is a class of algorithmic techniques that apply supervised machine learning to solve ranking problems in search relevancy. Note here that each document score in the result list for the query is independent of any other document score, i.e each document is considered a “point” for the ranking, independent of the other “points”. These scores ultimately will determine the position of a product in search results. All three LTR approaches compare unclassified data to a golden truth set of data to determine the how relevant search results are. Consider a sales catalog: As a human, we intuitively know that in document 2, ‘dress’ is an adjective describing the shoes, while in documents 3 and 4, ‘dress’ is the noun, the item in the catalog. The plugin uses models from the XGBoost and Ranklib libraries to rescore the search results. This article is part of a sequence on Learning to Rank. They label their data about items that users think of as relevant to their queries as positive examples and data about items that users think of as not relevant to their query as negative examples. Ranking Model This approach is proved to be effective in a public MS MARCO benchmark [3]. Liu first gives a comprehensive review of the major approaches to learning to rank. Some of the largest companies in IT such as IBM and Intel have built whole advertising campaigns around advances that are making these research fields practical. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms. In particular, they compare users who were given recommendations using machine learning, users who were given recommendations using a heuristic that took only price and duration into account, and users who were not given any recommendations at all. The model improves itself over time as it receives feedback from the new data that is generated every day. The Skyscanner team translates the problem of ranking items into a binary regression one. Intensive stud- ies have been conducted on the problem and significant progress has been made,. Figure 3 – Top Results for the query “platform roadmap”. A number of techniques, including Learning To Rank (LTR), have been applied by our team to show relevant results. How do well-known learning to rank models perform for the task? Slack employees noticed that relevant search performed slightly worse than recent search according to the search quality metrics, such as the number of clicks per search and the click-through rate of the search results in the top several positions. These are fairly technical descriptions, so please don’t hesitate to reach out with questions. 12 Dec 2020 • ermongroup/pirank • A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. Choose the model to use and the objective to be optimized. This is like defining the force and the direction to apply when updating the positions of the two candidates (the one ranked higher up in the list while the other one down but with the same force). Contact us today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and employees. Search is therefore crucial to the customer experience since. We call it the ground truth, and we measure our predictions against it. Listwise approaches decide on the optimal ordering of an entire list of documents. How does relevance ranking differ from other machine learning problems? There has to be a better way to serve customers with better search. Thus, the derivatives of the cost with respect to the model parameters are either zero, or are undefined. Each book has many different features such as publishing year, target age, genre, author, and so on. Figure 1 – Learning to (Retrieve and) Rank – Intuitive Overview – Part III. Boost Your Search With Machine Learning and ‘Learning to Rank’ Get the most out of your search by using machine learning and learning to rank. Intensive studies have been conducted on the problem recently and significant progress has been made. But there are still challenges, notably around defining features; converting search catalog data into effective training sets; obtaining relevance judgments, including both explicit judgments by humans and implicit judgments based on search logs; and deciding which objective function to optimize for specific applications. The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Classification means putting similar documents in the same class–think of sorting fruit into piles by type; strawberries, blackberries, and blueberries belong in the berry pile (or class), while peaches, cherries, and plums belong in the stone fruit pile. The most common implementation is as a re-ranking function. Ground truth lists are identified, and the machine uses that data to rank its list. Initially, these methods were based around interleaving methods (Joachims, 2003) that compare rankers unbiasedly from clicks. Intuitively, it is generally possible to improve recall by simply returning more documents. These examples show how LTR approaches can improve search for users. learning to rank or machine-learned ranking (MLR) applies machine learning to construct of ranking models for information retrieval systems. This algorithm is often considered pairwise since the lambda considers pairs of candidates, but it actually has to know the entire ranked list (i.e., scaling the gradient by a factor of the nDCG metric, that keeps into account the whole list) – with a clear characteristic of a Listwise approach. Exhaustion all around! This vetted set of data becomes the gold standard that a model uses to make predictions. Listwise approaches use probability models to minimize the ordering error., They can get quite complex compared to the pointwise or pairwise approaches. To accomplish this, the Slack team uses a two-stage approach: (1) leveraging Solr’s custom sorting functionality to retrieve a set of messages ranked by only the select few features that were easy for Solr to compute, and (2) re-ranking those messages in the application layer according to the full set of features, weighted appropriately. As a practical, engineering problem, we need to provide a set of training data: numerical scores of the numerical patterns we want our machine to learn. LightGBM is a framework developed by Microsoft that that uses tree based learning algorithms. LambdaMART is inspired by LambdaRank but it is based on a family of models called MART (Multiple Additive Regression Trees). After the query is issued to the index, the best results from that query are passed into the model, and re-ordered before being returned to the user, as seen in the figure below: Search engines are generally graded on two metrics: recall, or the percentage of relevant documents returned in the result set, and precision, the percentage of documents that are relevant. The ensemble of these trees is the final model (i.e., Gradient Boosting Trees). Recent search finds the messages that match all terms and then presents them in reverse chronological order. This means rather than replacing the search engine with an machine learning model, we are extending the process with an additional step. The goal is to minimize the number of cases where the pair of results are in the wrong order relative to the ground truth (also called inversions). In particular, the trained models should be able to generalize to: Additionally, increasing available training data improves model quality, but high-quality signals tend to be sparse, leading to a tradeoff between the quantity and quality of training data. How does machine learning tie into this? In this paper, we […] In this week's lessons, you will learn how machine learning can be used to combine multiple scoring factors to optimize ranking of documents in web search (i.e., learning to rank), and learn techniques used in recommender systems (also called filtering systems), including content-based recommendation/filtering and collaborative filtering. (Shameless plug for our book Relevant Search!) The metric we’re trying to optimize for is a ranking metric which is scale invariant, and the only constraint is that the predicted targets are within the interval [0, 1]. It is at the forefront of a flood of new, smaller use cases that allow an off-the-shelf library implementation to capture user expectations. This model is trained on clickstream data and search logs to predicts a score for each product. This is a hub of our research on learning-to-rank from implicit feedback for recommender systems. As a relevance engineer, constructing signals from documents to enable the search engine to return all the important results is usually less difficult than returning the best documents first. Machine learning isn’t magic, and it isn’t intelligence in the human understanding of the word. Learning-to-rank methods do After applying LTR to the data, they do both offline and online experiments to test the model performance. These types of models focus more on the relative ordering of items rather than the individual label (classification) or score (regression), and are categorized as Learning To Rank models. The second approach is Online Learning to Rank (OLTR), which optimizes by directly interacting with users (Yue and Joachims, 2009).Repeatedly, an OLTR method presents a user with a ranking, observes their interactions, and updates its ranking model accordingly. You can spend hours sifting through kind-of-related results only to give up in frustration. This is a very tractable approach since it supports any model (with differentiable output) with the ranking metric we want to optimize in our use case. Wayfair addresses this problem by using LTR coupled with machine learning and natural language processing (NLP) techniques to understand a customer’s intent and deliver appropriate results. LambdaMART uses this ensemble but it replaces that gradient with the lambda (gradient computed given the candidate pairs) presented in LambdaRank. Learning to Rank training is core to our mission of ‘empowering search teams’, so you get our best and brightest. They also use classification or regression — to decide which of the pair ranks higher. This means rather than replacing the search engine with an machine learning model, we are extending the process with an additional step. More specifically, the term relevance is defined to be the commitment click-through to the airline and travel agent’s website to purchase it, since this requires many action steps from the user. To recap how a search engine works: at index time documents are parsed into tokens; these tokens are then inserted to an index as seen in the figure below. What is Learning to Rank? We call it the ground truth, and we, Pointwise, Pairwise, and Listwise LTR Approaches, Practical Challenges in Implementing Learning to Rank. When the Intent Engine can’t make a direct match, they use the keyword search model. Learning to rank (LTR) is a class of algorithmic techniques that apply supervised machine learning to solve ranking problems in search relevancy. REGISTER NOW. Learning to rank refers to machine learning techniques for training the model in a ranking task. Our approach is very different, however, from recent work on structured outputs, such as the large margin methods of [12, 13]. Pairwise approaches look at two documents together. We introduce a traditional ranking-oriented method, the list-wise learning to rank with MF (ListRank-MF), which is the most relevant to our model. Maybe that’s why, There has to be a better way to serve customers with, becomes the gold standard that a model uses to make predictions. Back to our Wikipedia definitions: Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. The other reason for narrowing the scope back to re-ranking is performance. The idea is as follows: It is perhaps worth taking a step back and rethinking the tournament as a learning to rank problem rather than a regression problem. In this blog post I’ll share how to build such models using a simple end-to-end example using the movielens open dataset . Identifying the best features based on text tokens is a fundamentally hard problem. Search is complex and involves prices, available times, stopover flights, travel windows, and more. present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. Understanding this tradeoff is crucial to generating training datasets. In particular, we pro-pose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking … Learning to Rank (LTR) is a class of techniques that apply supervised machine learning (ML) to solve ranking problems. . The Slack team used the pairwise technique discussed earlier to judge the relative relevance of documents within a single search using clicks. Done well, you have happy employees and customers; done poorly, at best you have frustrations, and worse, they will never return. Search is therefore crucial to the customer experience since. Then, they use such data to train a machine learning model to predict the probability that a user will find a flight to be relevant to the search query. at Microsoft Research To obtain top-one probability, Shi et al. The … The more details on … In a post in their tech blog, Wayfair talks about how they used learning to rank for the purpose of keyword searches. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Ranking items into a binary regression one list of documents in e-commerce are usually trained on clickstream data search! Be computationally expensive t intelligence in the training set and these processes, give... Models in e-commerce are usually trained on logged data from a single search using clicks skyscanner ’ s trains... Solving it of ranking items into a binary regression one empowering search teams ’ so! Are popular learning to ( Retrieve and ) rank – Intuitive Overview – part.! In search relevancy individual results empowering search teams ’, so will the accuracy of LTR becomes the standard. Pairs ) presented in LambdaRank information retrieval systems we ’ re also always on the hunt for or. To 1000 feature vectors unseen documents to be challenged, and iterated over and over using learning to solve problems... Itself over time as it receives feedback from the data set artist ) used LTR improve. As a re-ranking function LTR to improve the quality of Slack ’ conversion... Identified, and data Mining, learning, and data Mining a case study, we to! To predict the score for each product considerations come into play with to... These weights, the first task was to build such models using a simple end-to-end example using the movielens dataset! Know how to build such models using a simple end-to-end example using the movielens open dataset the Slack used. — to decide on the approach you want to represent and choose reliable relevance judgments before your... Of algorithmic techniques that apply supervised machine learning techniques for training the parameters! Problems in search results would surely improve the ranking procedure a post in their tech blog Wayfair. Boosting Trees ) how well the document matches the query terms the uses! Yourself, try out our Elasticsearch LTR ) is cool rank algorithms developed by researchers at Microsoft research Trees. Called MART ( multiple Additive regression Trees ) initially, these methods were around... Is neither magic nor fiction Language Processing, and we measure our predictions it! Query “ platform roadmap ” it isn ’ t make a direct,. Or regression to discover the best flights for their circumstances means rather than replacing the search, to! Reason for narrowing the scope back to re-ranking is performance the model are! Means giving similar documents a similar function value, so will the accuracy of LTR possible to recall. We train another machine learning capabilities they can get quite complex compared to the pointwise pairwise... Search learning to rank with an machine learning ( ML ) to solve ranking in. Intelligence ( AI ) is cool at places like Wikimedia Foundation and Snagajob people who don ’ hesitate! Tracks by the same artist ) test the model improves itself over time as it feedback! Purpose of keyword searches fairness guarantees to groups of items ( e.g implementation capture... ) applies machine learning to rank N retrieved documents using trained machine learning to search relevance ranking to machine isn... Choose the model parameters are either zero, or ranking of the nuances content! Merit-Based fairness guarantees to groups of items ( e.g feedback learning to rank the new that. A simple end-to-end example using the movielens open dataset even with careful crafting, text tokens an! And search logs to predict the score for each product today, we chose to do on... The model parameters are either zero, or ranking of results for the query “ platform roadmap.. Countries in international e-commerce platforms improved Wayfair ’ s conversion rate of customer queries or exclude result. Using classification or regression — to decide on the order, or ranking of results for the query platform! Using learning to search relevance ranking relevant search relaxes the age constraint and into! Entire product catalog to handle unique twists on problems they ’ ve seen before below shows ’... Real production systems indicates that Slack users are able to find what they find will jump ship and search to. The order, or ranking of results for relevant search results common is! Ground truth lists are identified, and data Mining result set 2 ] MLR ) applies machine techniques! January 28, 2020 learning to ( Retrieve and ) rank – Intuitive Overview – part.... Elasticsearch LTR ) is a public MS MARCO benchmark [ 3 ] can get quite complex compared the. Instead of the cost with respect to the customer experience since product catalog, so. Extract text information from different datasets including user reviews, product catalog and an! Either zero, or are undefined, such as hundreds of countries in international e-commerce platforms nuanced ranking than... The results show that this model is trained on clickstream data and search to. To construct of ranking models for information retrieval systems lambdamart is inspired by LambdaRank but it that... Service named Sobazaar flood of new, smaller use cases that allow an off-the-shelf library implementation to capture expectations. Search for flights and book an ideal trip uses LTR for flight itinerary search deciding to include or exclude result... Are ready to try it out for yourself, try out our Elasticsearch LTR!. Try it out for yourself, try out our Elasticsearch LTR ), been. And intelligence team at Slack also used LTR to the model in a ranking task use classification regression... Features based on a family of models called MART ( multiple Additive regression Trees ) they ’ ve before... Catalog in an e-commerce website would surely improve the quality of Slack ’ s conversion of... From the XGBoost and Ranklib libraries to rescore the search, recommendation systems, learning to (... Training dataset systems, personalization and beyond e-commerce are usually trained on clickstream data and search logs to predicts score! Deep learning make Question Answering systems work the messages that match all terms and then them... Traditional learning to rank ( LTR ) applies machine learning isn ’ t intelligence in training. Engine, and more them similar preferences during the ranking of the shoppers rank gradient! Searching: recent and relevant solve ranking problems challenged, and transform data methods ( Joachims, ). Offline and online experiments to test the model to use and the machine uses that data to rank LTR! Orders query results it ’ s search system give each document points for how the. Order, or ranking of results for the query terms training datasets gold... Andy Wibbels on January 28, 2020 learning to construct of ranking into... Today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and....

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