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Found 6 papers, 1 papers with code
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.
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.
