LinkedIn Relevance Framework
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A LinkedIn Relevance Framework is a relevance framework used by a LinkedIn system to present LinkedIn displayable items (within a LinkedIn service).
References
2016
- (Zhang et al., 2016) ⇒ XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. (2016). “GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction.” In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-4232-2 doi:10.1145/2939672.2939684
- QUOTE: … We deployed the model in the LinkedIn job recommender system, and generated 20% to 40% more job applications for job seekers on LinkedIn.
2013
- http://sloanreview.mit.edu/article/the-relevance-of-data-going-behind-the-scenes-at-linkedin/
- QUOTE: Since the inventory of items we can display to users (e.g., feed updates, ads, news, people, jobs and others) is selected from a very large and dynamic pool, it is infeasible to select the best items for every user visit manually. We have built sophisticated machine learning and optimization algorithms to automatically recommend the best “items” to users in a given context at scale. Such automation improves the relevancy of products at low marginal cost and hence contributes to the bottom line. The algorithms we use are able to combine various data sources to perform such recommendations. We are fortunate to have rich profile data about our users; we know who they are connected to, and we understand past user interactions on various devices. For users who visit very often, we are able to provide deeply personalized recommendations. The sporadic visitors are automatically grouped into homogeneous cohorts by algorithms, and we provide best recommendations for each such cohort. We adapt our recommendations in real time based on what our users have consumed in the past. The entire end-to-end machinery has to work together to improve the relevance of our products.