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Found 12 papers, 1 papers with code
Embedding-based Recommender System for Job to Candidate Matching on Scale
The overall improvement of our job to candidate matching system has demonstrated its feasibility and scalability at a major online recruitment site.
Automated Discovery and Classification of Training Videos for Career Progression
Job transitions and upskilling are common actions taken by many industry working professionals throughout their career.
Tripartite Vector Representations for Better Job Recommendation
Job recommendation is a crucial part of the online job recruitment business.
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core.
Macro-optimization of email recommendation response rates harnessing individual activity levels and group affinity trends
Recommendation emails are among the best ways to re-engage with customers after they have left a website.
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain
This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph.
Application of Statistical Relational Learning to Hybrid Recommendation Systems
In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system.
Entity Type Recognition using an Ensemble of Distributional Semantic Models to Enhance Query Understanding
The data set contains entities extracted from millions of job seekers/recruiters search queries, job postings, and resume documents.
Sentiment/Subjectivity Analysis Survey for Languages other than English
This paper surveys different ways used for building systems for subjective and sentiment analysis for languages other than English.
GELATO and SAGE: An Integrated Framework for MS Annotation
The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO).
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems
When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node.
