1,549 research outputs found
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Conversational recommender systems (CRSs) aim to provide recommendation
services via natural language conversations. Although a number of approaches
have been proposed for developing capable CRSs, they typically rely on
sufficient training data for training. Since it is difficult to annotate
recommendation-oriented dialogue datasets, existing CRS approaches often suffer
from the issue of insufficient training due to the scarcity of training data.
To address this issue, in this paper, we propose a CounterFactual data
simulation approach for CRS, named CFCRS, to alleviate the issue of data
scarcity in CRSs. Our approach is developed based on the framework of
counterfactual data augmentation, which gradually incorporates the rewriting to
the user preference from a real dialogue without interfering with the entire
conversation flow. To develop our approach, we characterize user preference and
organize the conversation flow by the entities involved in the dialogue, and
design a multi-stage recommendation dialogue simulator based on a conversation
flow language model. Under the guidance of the learned user preference and
dialogue schema, the flow language model can produce reasonable, coherent
conversation flows, which can be further realized into complete dialogues.
Based on the simulator, we perform the intervention at the representations of
the interacted entities of target users, and design an adversarial training
method with a curriculum schedule that can gradually optimize the data
augmentation strategy. Extensive experiments show that our approach can
consistently boost the performance of several competitive CRSs, and outperform
other data augmentation methods, especially when the training data is limited.
Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR
Does intentional mean hierarchical? Knowledge flows and innovative performance of European regions
The production of scientific and technical knowledge is mostly concentrated in specific locations (high-tech clusters, innovative industry agglomerations, centres of excellence, and technologically advanced regions). Knowledge flows very easily within regions; however, scientific and technical knowledge also flow between regions. The aim of this paper was to analyse how knowledge flows between regions, and the effect of these flows on the innovative performance, measured by patent applications. We estimate a regional knowledge production function, and, using appropriate spatial econometric estimation techniques, we test the effect of both geographical and relational autocorrelation (measured by participation in EU funded research networks as part of Fifth Framework Programme). We model unobservable structure and link value of knowledge flows in these joint research networks. We find that knowledge flows within inter-regional research networks, along non-symmetrical and hierarchical structures in which the knowledge produced by network participants tends to be exploited by the network coordinator
A Comprehensive Survey on Generative Diffusion Models for Structured Data
In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its reviews on
structured data modelling via diffusion models, compared to other data
modalities such as visual and textual data. To address this gap, we present a
comprehensive review of recently proposed diffusion models in the field of
structured data. First, this survey provides a concise overview of the
score-based diffusion model theory, subsequently proceeding to the technical
descriptions of the majority of pioneering works that used structured data in
both data-driven general tasks and domain-specific applications. Thereafter, we
analyse and discuss the limitations and challenges shown in existing works and
suggest potential research directions. We hope this review serves as a catalyst
for the research community, promoting developments in generative diffusion
models for structured data.Comment: 20 pages, 1 figure, 2 table
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