Co-authors: Vivek Nelamangala and Val Markovic Editor’s note: In Part 1 of this series, we talked about how we built the Activity Graph and how we use it to deliver features to Activity searcher machines. In this post, we’ll talk about how we store this data for efficient storage and retrieval. Quick background on FollowFeed FollowFeed is the main Activity...
relevance Articles
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- Topics:
- relevance,
- Feed Personalization
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Co-authors: Val Markovic and Vivek Nelamangala Serving a feed of relevant, personalized content to 500 million members is a massive undertaking. Accordingly, our feed infrastructure is constantly evolving to take advantage of new relevance models, new features, and more efficient ways of scaling our infrastructure. In this post, we describe the Activity Graph,...
- Topics:
- relevance,
- Feed Personalization
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Co-authors: Rushi Bhatt and Bari Saltman LinkedIn’s home feed is the starting point of a member’s journey on LinkedIn. Millions of members see the feed as their first impression. The feed is also instrumental to delivering on LinkedIn’s core value propositions: it allows members to stay connected with their network by following network activities, to build their...
- Topics:
- relevance,
- machine learning,
- Feed Personalization
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Coauthors: Shivani Rao, Gungor Polatkan We recently launched LinkedIn Learning, an online learning platform that enables students and...
- Topics:
- machine learning,
- relevance,
- Product Design
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We believe LinkedIn can offer personalized insights into job opportunities in a way that no other website can. On our new Job Details...
- Topics:
- relevance
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Machine learning is a key component of LinkedIn’s relevance-driven products. We use machine learning to train the ranking algorithms...
- Topics:
- Spark,
- Relevance Science,
- Dr. Elephant,
- Open Source,
- relevance,
- Photon ML,
- machine learning