The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.
Statistical Smoothing : The idea of Bayesian hierarchical statistical smoothing originates from the smoothing of sparse events (e.g., CTR) in the context of computational advertising @cite_8 @cite_4 , where a natural hierarchy for the combination of ads category and publisher category is used for an (ad, publisher) pair with very little data to borrow strength from its ancestor nodes. However, that is an entirely different context and the models used are hence different.
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Online advertisement is a significant element of the Web browsing experience. A good advertising can not only bring benefits to publisher but also improve user satisfaction and extends advertiser's product marketing. To satisfy the desire of all three parties, the click through rate (CTR) prediction of a user to a specified ad in a specific context is of great importance. This challenging problem plays a key role in online advertising system and has to deal with several hard issues. Firstly, the model must process very high dimensional features from frequently changing ad, user and context, most of which are category features having large cardinality and sparse nature extending the dimensionality by two orders of magnitude. Secondly, nonlinear features such as conjunction information must be integrated into the model for a better prediction accuracy. Finally, the model must be able to parallelized efficiently to train from very large scale data sets. To address these problems, we proposed a novel model called Coupled Logistic Regression (CLR), for accurate and efficient CTR prediction. CLR can exploit all features from ad, user, context and nonlinear features among them by seamlessly integrate the conjunction information by employing factorization machine to achieve precise prediction result. And the high-dimensional problem is avoided by decomposing the decision function into two sub ones. Scalability of CLR is ensured through a newly invited MapReduce parallelization strategy, which can reduce communication and waiting time between nodes. Experimental results on real-world data set show that our CLR model can guarantee both accuracy and efficiency on large scale CTR prediction problems. In online advertising campaigns, to measure purchase propensity, click-through rate (CTR), defined as a ratio of number of clicks to number of impressions, is one of the most informative metrics used in business activities such as performance evaluation and budget planning. No matter what channel an ad goes through (display ads, sponsored search or contextual advertising), CTR estimation for rare events is essential but challenging, often incurring with huge variance, due to the sparsity in data. In this chapter, to alleviate this sparsity, we develop models and methods to smoothen CTR estimation by taking advantage of the data hierarchy in nature or by clustering and data continuity in time to leverage information from data close to the events of interest. In a contextual advertising system running at Yahoo!, we demonstrate that our methods lead to significantly more accurate estimation of CTRs. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult but yet need solving for a real-world implementation of the approach. We provide results from real advertising campaigns to demonstrate the effectiveness of our proposed approach.