ºÝºÝߣshows by User: SudeepDasPhD / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: SudeepDasPhD / Fri, 29 Jul 2022 23:22:57 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: SudeepDasPhD Query Facet Mapping and its Applications in Streaming Services: The Netflix Case Study /slideshow/query-facet-mapping-and-its-applications-in-streaming-services-the-netflix-case-study/252364516 sigir2022talk4-220729232257-ad53ff67
In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.]]>
In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.]]> Fri, 29 Jul 2022 23:22:57 GMT /slideshow/query-facet-mapping-and-its-applications-in-streaming-services-the-netflix-case-study/252364516 SudeepDasPhD@slideshare.net(SudeepDasPhD) Query Facet Mapping and its Applications in Streaming Services: The Netflix Case Study SudeepDasPhD In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sigir2022talk4-220729232257-ad53ff67-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.
Query Facet Mapping and its Applications in Streaming Services: The Netflix Case Study from Sudeep Das, Ph.D.
]]> 129 0 https://cdn.slidesharecdn.com/ss_thumbnails/sigir2022talk4-220729232257-ad53ff67-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 Personalization at Netflix - Making Stories Travel /slideshow/personalization-at-netflix-making-stories-travel-170353196/170353196 personalizationatnetflixmakingstoriestravel-190909221835
I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world. ]]>
I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world. ]]> Mon, 09 Sep 2019 22:18:35 GMT /slideshow/personalization-at-netflix-making-stories-travel-170353196/170353196 SudeepDasPhD@slideshare.net(SudeepDasPhD) Personalization at Netflix - Making Stories Travel SudeepDasPhD I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/personalizationatnetflixmakingstoriestravel-190909221835-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world.
Personalization at Netflix - Making Stories Travel from Sudeep Das, Ph.D.
]]> 2238 3 https://cdn.slidesharecdn.com/ss_thumbnails/personalizationatnetflixmakingstoriestravel-190909221835-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 Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search /slideshow/deeper-things-how-netflix-leverages-deep-learning-in-recommendations-and-search/131390360 deeperthings1-190211211259
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges. ]]>
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges. ]]> Mon, 11 Feb 2019 21:12:59 GMT /slideshow/deeper-things-how-netflix-leverages-deep-learning-in-recommendations-and-search/131390360 SudeepDasPhD@slideshare.net(SudeepDasPhD) Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search SudeepDasPhD In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeperthings1-190211211259-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search from Sudeep Das, Ph.D.
]]> 13613 10 https://cdn.slidesharecdn.com/ss_thumbnails/deeperthings1-190211211259-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 Academia to Data Science - A Hitchhiker's Guide /slideshow/academia-to-data-science-a-hitchhikers-guide/89309574 mdih7c7gql2cwj8tqjgy-signature-fc1f25a162b2774e5a1643b569403ca0e8abe9750658c24dee8563ea6b6d9fed-poli-180302000436
Recently I gave a talk at UC Berkeley regarding the transition from academia to industry in the context of Machine Learning and Data Science related roles. I based most of my slides on my own transition from being an Astrophysicist to a Machine Learning Expert. I hope this will be useful to many. Feedback is welcome! ]]>
Recently I gave a talk at UC Berkeley regarding the transition from academia to industry in the context of Machine Learning and Data Science related roles. I based most of my slides on my own transition from being an Astrophysicist to a Machine Learning Expert. I hope this will be useful to many. Feedback is welcome! ]]> Fri, 02 Mar 2018 00:04:35 GMT /slideshow/academia-to-data-science-a-hitchhikers-guide/89309574 SudeepDasPhD@slideshare.net(SudeepDasPhD) Academia to Data Science - A Hitchhiker's Guide SudeepDasPhD Recently I gave a talk at UC Berkeley regarding the transition from academia to industry in the context of Machine Learning and Data Science related roles. I based most of my slides on my own transition from being an Astrophysicist to a Machine Learning Expert. I hope this will be useful to many. Feedback is welcome! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mdih7c7gql2cwj8tqjgy-signature-fc1f25a162b2774e5a1643b569403ca0e8abe9750658c24dee8563ea6b6d9fed-poli-180302000436-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Recently I gave a talk at UC Berkeley regarding the transition from academia to industry in the context of Machine Learning and Data Science related roles. I based most of my slides on my own transition from being an Astrophysicist to a Machine Learning Expert. I hope this will be useful to many. Feedback is welcome!
Academia to Data Science - A Hitchhiker's Guide from Sudeep Das, Ph.D.
]]> 4522 6 https://cdn.slidesharecdn.com/ss_thumbnails/mdih7c7gql2cwj8tqjgy-signature-fc1f25a162b2774e5a1643b569403ca0e8abe9750658c24dee8563ea6b6d9fed-poli-180302000436-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 Crafting Recommenders: the Shallow and the Deep of it! /slideshow/crafting-recommenders-the-shallow-and-the-deep-of-it/86661257 fads-xian2-180124223740
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution! ]]>
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution! ]]> Wed, 24 Jan 2018 22:37:40 GMT /slideshow/crafting-recommenders-the-shallow-and-the-deep-of-it/86661257 SudeepDasPhD@slideshare.net(SudeepDasPhD) Crafting Recommenders: the Shallow and the Deep of it! SudeepDasPhD I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fads-xian2-180124223740-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution!
Crafting Recommenders: the Shallow and the Deep of it! from Sudeep Das, Ph.D.
]]> 1986 5 https://cdn.slidesharecdn.com/ss_thumbnails/fads-xian2-180124223740-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 2015: Making Meaningful Restaurant Recommendations at OpenTable /slideshow/recsys-2015-making-meaningful-restaurant-recommendations-at-opentable/53085817 recsys2015-150922223205-lva1-app6891
At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.]]>
At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.]]> Tue, 22 Sep 2015 22:32:05 GMT /slideshow/recsys-2015-making-meaningful-restaurant-recommendations-at-opentable/53085817 SudeepDasPhD@slideshare.net(SudeepDasPhD) Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTable SudeepDasPhD At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/recsys2015-150922223205-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.
Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTable from Sudeep Das, Ph.D.
]]> 8014 19 https://cdn.slidesharecdn.com/ss_thumbnails/recsys2015-150922223205-lva1-app6891-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-SudeepDasPhD-48x48.jpg?cb=1659896228 Recommendation and Search at Netflix! datamusing.info https://cdn.slidesharecdn.com/ss_thumbnails/sigir2022talk4-220729232257-ad53ff67-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/query-facet-mapping-and-its-applications-in-streaming-services-the-netflix-case-study/252364516 Query Facet Mapping an... https://cdn.slidesharecdn.com/ss_thumbnails/personalizationatnetflixmakingstoriestravel-190909221835-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/personalization-at-netflix-making-stories-travel-170353196/170353196 Personalization at Net... https://cdn.slidesharecdn.com/ss_thumbnails/deeperthings1-190211211259-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deeper-things-how-netflix-leverages-deep-learning-in-recommendations-and-search/131390360 Deeper Things: How Ne...