42,609 research outputs found
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Knowledge graphs (KGs) are commonly used as side information to enhance
collaborative signals and improve recommendation quality. In the context of
knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged
as promising solutions for modeling factual and semantic information in KGs.
However, the long-tail distribution of entities leads to sparsity in
supervision signals, which weakens the quality of item representation when
utilizing KG enhancement. Additionally, the binary relation representation of
KGs simplifies hyper-relational facts, making it challenging to model complex
real-world information. Furthermore, the over-smoothing phenomenon results in
indistinguishable representations and information loss. To address these
challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph
Recommendation based on Hyper-Relational Knowledge Graph) framework. This
framework establishes a cross-view hypergraph self-supervised learning
mechanism for KG enhancement. Specifically, we model hyper-relational facts in
KGs to capture interdependencies between entities under complete semantic
conditions. With the refined representation, a hypergraph is dynamically
constructed to preserve features in the deep vector space, thereby alleviating
the over-smoothing problem. Furthermore, we mine external supervision signals
from both the global perspective of the hypergraph and the local perspective of
collaborative filtering (CF) to guide the model prediction process. Extensive
experiments conducted on different datasets demonstrate the superiority of the
SDK framework over state-of-the-art models. The results showcase its ability to
alleviate the effects of over-smoothing and supervision signal sparsity
Cognitive system to achieve human-level accuracy in automated assignment of helpdesk email tickets
Ticket assignment/dispatch is a crucial part of service delivery business
with lot of scope for automation and optimization. In this paper, we present an
end-to-end automated helpdesk email ticket assignment system, which is also
offered as a service. The objective of the system is to determine the nature of
the problem mentioned in an incoming email ticket and then automatically
dispatch it to an appropriate resolver group (or team) for resolution.
The proposed system uses an ensemble classifier augmented with a configurable
rule engine. While design of classifier that is accurate is one of the main
challenges, we also need to address the need of designing a system that is
robust and adaptive to changing business needs. We discuss some of the main
design challenges associated with email ticket assignment automation and how we
solve them. The design decisions for our system are driven by high accuracy,
coverage, business continuity, scalability and optimal usage of computational
resources.
Our system has been deployed in production of three major service providers
and currently assigning over 40,000 emails per month, on an average, with an
accuracy close to 90% and covering at least 90% of email tickets. This
translates to achieving human-level accuracy and results in a net saving of
about 23000 man-hours of effort per annum
Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the preferences
and behaviors of users with respect to a set of items (e.g., movies, books,
academic papers). Typically, the latent factors are assumed to be static and,
given these factors, the observed preferences and behaviors of users are
assumed to be generated without order. These assumptions limit the explorative
and predictive capabilities of such models, since users' interests and item
popularity may evolve over time. To address this, we propose dPF, a dynamic
matrix factorization model based on the recent Poisson factorization model for
recommendations. dPF models the time evolving latent factors with a Kalman
filter and the actions with Poisson distributions. We derive a scalable
variational inference algorithm to infer the latent factors. Finally, we
demonstrate dPF on 10 years of user click data from arXiv.org, one of the
largest repository of scientific papers and a formidable source of information
about the behavior of scientists. Empirically we show performance improvement
over both static and, more recently proposed, dynamic recommendation models. We
also provide a thorough exploration of the inferred posteriors over the latent
variables.Comment: RecSys 201
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
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