�ݺ�ߣshows by User: AmanGrover9

�ݺ�ߣshows by User: AmanGrover9 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: AmanGrover9 / Sat, 12 Aug 2017 02:26:46 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: AmanGrover9 SIGIR 2017 - Candidate Selection for Large Scale Personalized Search and Recommender Systems /slideshow/sigir-2017-candidate-selection-for-large-scale-personalized-search-and-recommender-systems/78778427 candidateselectiontutorial-170812022646
Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency. GITHUB : https://github.com/candidate-selection-tutorial-sigir2017/candidate-selection-tutorial]]>
Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency. GITHUB : https://github.com/candidate-selection-tutorial-sigir2017/candidate-selection-tutorial]]> Sat, 12 Aug 2017 02:26:46 GMT /slideshow/sigir-2017-candidate-selection-for-large-scale-personalized-search-and-recommender-systems/78778427 AmanGrover9@slideshare.net(AmanGrover9) SIGIR 2017 - Candidate Selection for Large Scale Personalized Search and Recommender Systems AmanGrover9 Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency. GITHUB : https://github.com/candidate-selection-tutorial-sigir2017/candidate-selection-tutorial <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/candidateselectiontutorial-170812022646-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency. GITHUB : https://github.com/candidate-selection-tutorial-sigir2017/candidate-selection-tutorial

SIGIR 2017 - Candidate Selection for Large Scale Personalized Search and Recommender Systems from Aman Grover

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0 https://cdn.slidesharecdn.com/profile-photo-AmanGrover9-48x48.jpg?cb=1554450927 Currently working as Senior Relevance Engineer in Careers team focusing on improving jobs recommendation using wide and deep learning methods. Primarily interested in theory, algorithms, data mining , machine learning and information retrieval. Love Gadgets | Tech stuff | Cricket | Ping Pong | Inventions.