ºÝºÝߣshows by User: fobabel / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: fobabel / Thu, 06 Jul 2017 07:22:20 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: fobabel Machine Learning for Recommender Systems in the Job Market /slideshow/machine-learning-for-recommender-systems-in-the-job-market/77565688 2017-05-10-hamburg-ai-xing-fabian-170706072220
XING is a social network that aims at enabling professionals grow. In this talk, we give some insights into the machine learning pipelines that we use at XING for building recommender systems. We will focus on job recommendations and discuss challenges, architecture, features and algorithms that we use for recommending job ads to people and for understanding whether a person is actually willing to change jobs and an appropriate candidate for a given job. Talk at https://hamburg.city.ai/]]>
XING is a social network that aims at enabling professionals grow. In this talk, we give some insights into the machine learning pipelines that we use at XING for building recommender systems. We will focus on job recommendations and discuss challenges, architecture, features and algorithms that we use for recommending job ads to people and for understanding whether a person is actually willing to change jobs and an appropriate candidate for a given job. Talk at https://hamburg.city.ai/]]> Thu, 06 Jul 2017 07:22:20 GMT /slideshow/machine-learning-for-recommender-systems-in-the-job-market/77565688 fobabel@slideshare.net(fobabel) Machine Learning for Recommender Systems in the Job Market fobabel XING is a social network that aims at enabling professionals grow. In this talk, we give some insights into the machine learning pipelines that we use at XING for building recommender systems. We will focus on job recommendations and discuss challenges, architecture, features and algorithms that we use for recommending job ads to people and for understanding whether a person is actually willing to change jobs and an appropriate candidate for a given job. Talk at https://hamburg.city.ai/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017-05-10-hamburg-ai-xing-fabian-170706072220-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> XING is a social network that aims at enabling professionals grow. In this talk, we give some insights into the machine learning pipelines that we use at XING for building recommender systems. We will focus on job recommendations and discuss challenges, architecture, features and algorithms that we use for recommending job ads to people and for understanding whether a person is actually willing to change jobs and an appropriate candidate for a given job. Talk at https://hamburg.city.ai/
Machine Learning for Recommender Systems in the Job Market from Fabian Abel
]]> 686 6 https://cdn.slidesharecdn.com/ss_thumbnails/2017-05-10-hamburg-ai-xing-fabian-170706072220-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 RecSys Challenge 2016 /slideshow/recsys-challenge-2016/66257378 2016-09-15-recsys-workshop-introduction-160921130110
The ACM RecSys Challenge 2016 was focussing on the problem of job recommendations: given a user, return a ranked list of jobs that the user is likely to be interested in. More than 100 teams actively participated and submitted solutions. All the winning teams used an ensemble of recommender strategies (e.g. learning to rank approaches, matrix factorization techniques, etc.). More details: http://2016.recsyschallenge.com/ ]]>
The ACM RecSys Challenge 2016 was focussing on the problem of job recommendations: given a user, return a ranked list of jobs that the user is likely to be interested in. More than 100 teams actively participated and submitted solutions. All the winning teams used an ensemble of recommender strategies (e.g. learning to rank approaches, matrix factorization techniques, etc.). More details: http://2016.recsyschallenge.com/ ]]> Wed, 21 Sep 2016 13:01:10 GMT /slideshow/recsys-challenge-2016/66257378 fobabel@slideshare.net(fobabel) RecSys Challenge 2016 fobabel The ACM RecSys Challenge 2016 was focussing on the problem of job recommendations: given a user, return a ranked list of jobs that the user is likely to be interested in. More than 100 teams actively participated and submitted solutions. All the winning teams used an ensemble of recommender strategies (e.g. learning to rank approaches, matrix factorization techniques, etc.). More details: http://2016.recsyschallenge.com/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-09-15-recsys-workshop-introduction-160921130110-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The ACM RecSys Challenge 2016 was focussing on the problem of job recommendations: given a user, return a ranked list of jobs that the user is likely to be interested in. More than 100 teams actively participated and submitted solutions. All the winning teams used an ensemble of recommender strategies (e.g. learning to rank approaches, matrix factorization techniques, etc.). More details: http://2016.recsyschallenge.com/
RecSys Challenge 2016 from Fabian Abel
]]> 1172 3 https://cdn.slidesharecdn.com/ss_thumbnails/2016-09-15-recsys-workshop-introduction-160921130110-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 What's wrong with Recruiter-John? A non-trivial recommender challenge. /slideshow/whats-wrong-with-recruiterjohn-a-nontrivial-recommender-challenge/62996287 2016-06-09-recsys-meetup-budapest-160613075818
Some background about http://recsyschallenge.com Abstract: Maria from Illusion Inc. recently told her colleague Recruiter-John that she needs an additional team member: "I'm looking for a new team member that can support us in building up our new search engine. Some experience with Lucene or other search technologies and some background in information retrieval would be cool!". Recruiter-John then did his work and redirected her message to the world: "We are searching for a Data Scientist who has a PhD in Computer Science or some related field, 5 years industry experience in building search engines with Lucene at large scale, is highly skilled in Elixir and Erlang and fits into our young, dynamic team…". Paul is currently searching for a job: "I am a Senior Software Engineer with more than 10 years of industry experience in Java, Scala, C++ and 15 other programming languages, familiar with technologies such as Solr, Hadoop, Pig, Hive, Hbase, Cassandra, Riak, … I'm currently looking for a job where I can bring my dog to work." Bringing together the demand and offer on the job market is a non-trivial problem. In this talk, we will share some of our frustration and will describe how we tackle this problem with recommender systems on XING, a career-oriented social network. We will give insights on the RecSys challenge (http://recsyschallenge.com) which is our attempt to let the bright minds of the RecSys community solve the recommendation problem that we failed to solve properly over the past four years. :-)]]>
Some background about http://recsyschallenge.com Abstract: Maria from Illusion Inc. recently told her colleague Recruiter-John that she needs an additional team member: "I'm looking for a new team member that can support us in building up our new search engine. Some experience with Lucene or other search technologies and some background in information retrieval would be cool!". Recruiter-John then did his work and redirected her message to the world: "We are searching for a Data Scientist who has a PhD in Computer Science or some related field, 5 years industry experience in building search engines with Lucene at large scale, is highly skilled in Elixir and Erlang and fits into our young, dynamic team…". Paul is currently searching for a job: "I am a Senior Software Engineer with more than 10 years of industry experience in Java, Scala, C++ and 15 other programming languages, familiar with technologies such as Solr, Hadoop, Pig, Hive, Hbase, Cassandra, Riak, … I'm currently looking for a job where I can bring my dog to work." Bringing together the demand and offer on the job market is a non-trivial problem. In this talk, we will share some of our frustration and will describe how we tackle this problem with recommender systems on XING, a career-oriented social network. We will give insights on the RecSys challenge (http://recsyschallenge.com) which is our attempt to let the bright minds of the RecSys community solve the recommendation problem that we failed to solve properly over the past four years. :-)]]> Mon, 13 Jun 2016 07:58:18 GMT /slideshow/whats-wrong-with-recruiterjohn-a-nontrivial-recommender-challenge/62996287 fobabel@slideshare.net(fobabel) What's wrong with Recruiter-John? A non-trivial recommender challenge. fobabel Some background about http://recsyschallenge.com Abstract: Maria from Illusion Inc. recently told her colleague Recruiter-John that she needs an additional team member: "I'm looking for a new team member that can support us in building up our new search engine. Some experience with Lucene or other search technologies and some background in information retrieval would be cool!". Recruiter-John then did his work and redirected her message to the world: "We are searching for a Data Scientist who has a PhD in Computer Science or some related field, 5 years industry experience in building search engines with Lucene at large scale, is highly skilled in Elixir and Erlang and fits into our young, dynamic team…". Paul is currently searching for a job: "I am a Senior Software Engineer with more than 10 years of industry experience in Java, Scala, C++ and 15 other programming languages, familiar with technologies such as Solr, Hadoop, Pig, Hive, Hbase, Cassandra, Riak, … I'm currently looking for a job where I can bring my dog to work." Bringing together the demand and offer on the job market is a non-trivial problem. In this talk, we will share some of our frustration and will describe how we tackle this problem with recommender systems on XING, a career-oriented social network. We will give insights on the RecSys challenge (http://recsyschallenge.com) which is our attempt to let the bright minds of the RecSys community solve the recommendation problem that we failed to solve properly over the past four years. :-) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-06-09-recsys-meetup-budapest-160613075818-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Some background about http://recsyschallenge.com Abstract: Maria from Illusion Inc. recently told her colleague Recruiter-John that she needs an additional team member: &quot;I&#39;m looking for a new team member that can support us in building up our new search engine. Some experience with Lucene or other search technologies and some background in information retrieval would be cool!&quot;. Recruiter-John then did his work and redirected her message to the world: &quot;We are searching for a Data Scientist who has a PhD in Computer Science or some related field, 5 years industry experience in building search engines with Lucene at large scale, is highly skilled in Elixir and Erlang and fits into our young, dynamic team…&quot;. Paul is currently searching for a job: &quot;I am a Senior Software Engineer with more than 10 years of industry experience in Java, Scala, C++ and 15 other programming languages, familiar with technologies such as Solr, Hadoop, Pig, Hive, Hbase, Cassandra, Riak, … I&#39;m currently looking for a job where I can bring my dog to work.&quot; Bringing together the demand and offer on the job market is a non-trivial problem. In this talk, we will share some of our frustration and will describe how we tackle this problem with recommender systems on XING, a career-oriented social network. We will give insights on the RecSys challenge (http://recsyschallenge.com) which is our attempt to let the bright minds of the RecSys community solve the recommendation problem that we failed to solve properly over the past four years. :-)
What's wrong with Recruiter-John? A non-trivial recommender challenge. from Fabian Abel
]]> 578 8 https://cdn.slidesharecdn.com/ss_thumbnails/2016-06-09-recsys-meetup-budapest-160613075818-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Carrots for Couch Potatoes: Improving recommendations by motivating the crowd /slideshow/carrots-for-couch-potatoes-improving-recommendations-by-motivating-the-crowd/53097051 2015-recsys-crowdrec-workshop-fabian-150923073012-lva1-app6892
Some recommender systems exploit quite complex features and give users the impression that black magic is performed to provide somewhat meaningful recommendations. Users may moreover adopt a rather passive attitude: instead of actively interacting with the recommendations and providing feedback they take the quality of the recommendations for granted. In this talk, we discuss challenges of crowd-based recommender systems and present some strategies for involving people more actively into the flow of computing recommendations.]]>
Some recommender systems exploit quite complex features and give users the impression that black magic is performed to provide somewhat meaningful recommendations. Users may moreover adopt a rather passive attitude: instead of actively interacting with the recommendations and providing feedback they take the quality of the recommendations for granted. In this talk, we discuss challenges of crowd-based recommender systems and present some strategies for involving people more actively into the flow of computing recommendations.]]> Wed, 23 Sep 2015 07:30:11 GMT /slideshow/carrots-for-couch-potatoes-improving-recommendations-by-motivating-the-crowd/53097051 fobabel@slideshare.net(fobabel) Carrots for Couch Potatoes: Improving recommendations by motivating the crowd fobabel Some recommender systems exploit quite complex features and give users the impression that black magic is performed to provide somewhat meaningful recommendations. Users may moreover adopt a rather passive attitude: instead of actively interacting with the recommendations and providing feedback they take the quality of the recommendations for granted. In this talk, we discuss challenges of crowd-based recommender systems and present some strategies for involving people more actively into the flow of computing recommendations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2015-recsys-crowdrec-workshop-fabian-150923073012-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Some recommender systems exploit quite complex features and give users the impression that black magic is performed to provide somewhat meaningful recommendations. Users may moreover adopt a rather passive attitude: instead of actively interacting with the recommendations and providing feedback they take the quality of the recommendations for granted. In this talk, we discuss challenges of crowd-based recommender systems and present some strategies for involving people more actively into the flow of computing recommendations.
Carrots for Couch Potatoes: Improving recommendations by motivating the crowd from Fabian Abel
]]> 488 5 https://cdn.slidesharecdn.com/ss_thumbnails/2015-recsys-crowdrec-workshop-fabian-150923073012-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Recommending job ads to people /slideshow/recommending-job-ads-to-people/42558283 2014-12-recommending-job-ads-to-people-141210044545-conversion-gate02
Some insights about the job recommendation system that we were running on XING in the last years.]]>
Some insights about the job recommendation system that we were running on XING in the last years.]]> Wed, 10 Dec 2014 04:45:45 GMT /slideshow/recommending-job-ads-to-people/42558283 fobabel@slideshare.net(fobabel) Recommending job ads to people fobabel Some insights about the job recommendation system that we were running on XING in the last years. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014-12-recommending-job-ads-to-people-141210044545-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Some insights about the job recommendation system that we were running on XING in the last years.
Recommending job ads to people from Fabian Abel
]]> 1484 5 https://cdn.slidesharecdn.com/ss_thumbnails/2014-12-recommending-job-ads-to-people-141210044545-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams /slideshow/twitter-twinder-twitcident-filtering-and-search-in-social-web-streams/12524565 2012-04-paris-twitter-search-120413033022-phpapp01
ºÝºÝߣs presented at the EIT DataBridges Workshop in Paris: http://team.inria.fr/oak/share/workshop-databridges-april-12-2012/ ]]>
ºÝºÝߣs presented at the EIT DataBridges Workshop in Paris: http://team.inria.fr/oak/share/workshop-databridges-april-12-2012/ ]]> Fri, 13 Apr 2012 03:30:19 GMT /slideshow/twitter-twinder-twitcident-filtering-and-search-in-social-web-streams/12524565 fobabel@slideshare.net(fobabel) Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams fobabel ºÝºÝߣs presented at the EIT DataBridges Workshop in Paris: http://team.inria.fr/oak/share/workshop-databridges-april-12-2012/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012-04-paris-twitter-search-120413033022-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs presented at the EIT DataBridges Workshop in Paris: http://team.inria.fr/oak/share/workshop-databridges-april-12-2012/
Twitter, Twinder, Twitcident: Filtering and Search in Social Web Streams from Fabian Abel
]]> 576 4 https://cdn.slidesharecdn.com/ss_thumbnails/2012-04-paris-twitter-search-120413033022-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-fobabel-48x48.jpg?cb=1523098237 https://cdn.slidesharecdn.com/ss_thumbnails/2017-05-10-hamburg-ai-xing-fabian-170706072220-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-for-recommender-systems-in-the-job-market/77565688 Machine Learning for R... https://cdn.slidesharecdn.com/ss_thumbnails/2016-09-15-recsys-workshop-introduction-160921130110-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/recsys-challenge-2016/66257378 RecSys Challenge 2016 https://cdn.slidesharecdn.com/ss_thumbnails/2016-06-09-recsys-meetup-budapest-160613075818-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/whats-wrong-with-recruiterjohn-a-nontrivial-recommender-challenge/62996287 What&#39;s wrong with Recr...