Ganesh Venkataraman

Personalized expertise search at LinkedIn

Linkedln is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the problem of personalized expertise search on LinkedIn, particularly for exploratory search queries containing skills. In the offline phase, we introduce a collaborative filtering approach based on matrix factorization. Our approach estimates expertise scores for both the skills that members list on their profiles as well as the skills they are likely to have but do not explicitly list. In the online phase (at query time) we use expertise scores on these skills as a feature in combination with other features to rank the results. To learn the personalized ranking function, we propose a heuristic to extract training data from search logs while handling position and sample selection biases. We tested our models on two products - LinkedIn homepage and LinkedIn recruiter. A/B tests showed significant improvements in click through rates - 31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages sent from search - 37% for recruiter (20% for homepage). As of writing this paper, these models serve nearly all live traffic for skills search on LinkedIn homepage as well as LinkedIn recruiter.

Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned

Online professional social networks such as LinkedIn play a key role in helping job seekers find right career opportunities and job providers reach out to potential candidates. LinkedIns job ecosystem has been designed to serve as a marketplace for efficient matching between potential candidates and job postings, and to provide tools to connect job seekers and job providers. LinkedIns job recommendations product is a crucial mechanism to help achieve these goals, wherein personalized sets of recommended job postings are presented for members based on the structured, context data present in their profiles.

Latency Reduction via Decision Tree Based Query Construction

LinkedIn as a professional network serves the career needs of 450 Million plus members. The task of job recommendation system is to nd the suitable job among a corpus of several million jobs and serve this in real time under tight latency constraints. Job search involves nding suitable job listings given a user, query and context. Typical scoring function for both search and recommendations involves evaluating a function that matches various elds in the job description with various elds in the member pro le. This in turn translates to evaluating a function with several thousands of features to get the right ranking. In recommendations, evaluating all the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way to model the underlying complex ranking function via decision trees. The branches within the decision trees are query clauses and hence the decision trees can be mapped on to real time queries. We developed an o ine framework which evaluates the quality of the decision tree with respect to latency and recall. We tested the approach on job search and recommendations on LinkedIn and A/B tests show signi cant improvements in member engagement and latency. Our techniques helped reduce job search latency by over 67% and our recommendations latency by over 55%. Our techniques show 3.5% improvement in applications from job recommendations primarily due to reduced timeouts from upstream services. As of writing the approach powers all of job search and recommendations on LinkedIn.

Search Without a Query: Powering Job Recommendations via Search Index at LinkedIn

The mission of LinkedIn is to connect the worlds professionals to make them more productive and successful. LinkedIn operates the worlds largest professional network on the Internet with more than 500 Million members in over 200 countries. Core to realizing the mission is to help people find jobs. In this paper, we describe how the jobs recommendations is powered by a search index and some practical challenges involved in scaling such a system.

Large Scale Search Engine Marketing (SEM) at Airbnb

Airbnb is an online marketplace which connects hosts and guests all over the world. Our inventory includes over 4.5 million listings, which enable the travel of over 300 million guests. The growth team at Airbnb is responsible for helping travelers find Airbnb, in part by participating in ad auctions on major search platforms such as Google and Bing. In this talk, we will describe how ad- vertising efficiently on these platforms requires solving several information retrieval and machine learning problems, including query understanding, click value estimation, and realtime pacing of our expenditure.

Candidate Selection for Large Scale Personalized Search and Recommender Systems

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.

Fast, lenient and accurate: Building personalized instant search experience at LinkedIn

Instant search has become a common part of the search experience in most popular search engines and social networking websites. The goal is to provide instant feedback to the user in terms of query completions (“instant suggestions”) or directly provide search results (“instant results”) as the user is typing their query. In this paper, we describe the challenges that we faced while delivering the instant search experience at LinkedIn, and present techniques that we developed to overcome them. We discuss three aspects of instant search — performance, tolerance to user errors and accuracy. On the performance side, we discuss our inverted index ordering scheme, which when combined with query rewriting and early termination techniques, helped us significantly reduce latency while maintaining good search recall. We describe our method to handle hard to spell or misspelled names using name clusters. We also present ranking methods that leverage the structured nature of queries and documents at LinkedIn. All the methods described have been fully deployed in production, and have helped us significantly improve the search experience for our members. Specifically, we increased the number of queries served via typeahead by 39.53%.