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์คํํธ๋ด ์ด์ค ๊ตฐ์งํ๋ฅผ ์ด์ฉํ ๊ทธ๋ํ๊ธฐ๋ฐ ํ์ ํํฐ๋ง์ ๊ตญ์ง ์์๋ธ ๋ฐฉ๋ฒ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์์ฐ๊ณผํ๋ํ ํ๋๊ณผ์ ๊ณ์ฐ๊ณผํ์ ๊ณต, 2022. 8. ๊ฐ๋ช
์ฃผ.The importance of a personalized recommendation system is emerging as the world becomes more complex and individualized. Among various recommendation systems, Neural Graph Collaborative Filtering(NGCF) and its variants treat the user-item set as a bipartite graph and learn the interactions between user and item without using their unique features. While these approaches only using collaborative signals have achieved state-of-the-art performance, they still have the disadvantage of abandoning feature similarity among users and items. To tackle this problem, we adopt unsupervised community detection from bipartite graph structure to enhance the collaborative signal for a Graph-based recommendation system. Co-Clustering algorithms segment the user-item matrix into small groups. Each local CF captures a strong correlation among these local user-item subsets, while the original incidence matrix is also used to analyze global interaction between groups. Finally, our Local-Ensemble Graph Collaborative Filtering(LEGCF) aggregates all local and global collaborative information. As the proposed approach can utilize various Co-clustering and Collaborative Filtering flexibly, one of the most straightforward variants, Spectral Co-Clustering and NGCF, can enhance the overall performance.๋ณธ ๋
ผ๋ฌธ์์๋ ์ถ์ฒ์์คํ
์ ์ํ ๊ทธ๋ํ ๊ธฐ๋ฐ ํ์
ํํฐ๋ง ๋ชจ๋ธ์ ์คํํธ๋ด ์ด์ค ๋ถํ ํ์ฌ ์์ฑ๋ ๋ถ๋ถ ๊ทธ๋ํ๋ฅผ ์์๋ธ(Ensemble) ํ์ฌ ์ถ์ฒ ์ฑ๋ฅ์ ๊ฐ์ ํ๋ ๋ฐฉ๋ฒ์ ๋ํด ์ฐ๊ตฌํ์๋ค. ๊ทธ๋ํ ์ธ๊ณต ์ ๊ฒฝ๋ง (Graph Neural Networks, GNN)์ ์ด์ฉํ ํ์
ํํฐ๋ง ๊ธฐ๋ฐ์ ์ถ์ฒ ์์คํ
์ ๊ธฐ๋ณธ ๋ชจ๋ธ์, ์ฌ์ฉ์๋ ์์ดํ
์ ๋ํ ์ฌ์ ์ ๋ณด๋ฅผ ์ ํ ์ฌ์ฉํ์ง ์๊ณ ์ฌ์ฉ์-์์ดํ
๊ฐ ์ํธ์์ฉ ์ ๋ณด๋ง์ ํ์ฉํ์ฌ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ์๋ฒ ๋ฉ์ ๊ตฌ์ฑํ๋ค. ๋ฐ๋ผ์ ์ฌ์ฉ์์ ์์ดํ
์ ์ฌ์ ์ ๋ณด๋ง์ผ๋ก ์ ์ถํ ์ ์๋ ํน์ ์ฌ์ฉ์ ๊ทธ๋ฃน์ ๊ฒฝํฅ์ฑ์ ์ถ์ฒ์์คํ
์ ์ฌ์ฉํ ์ ์๋ ๋จ์ ์ด ์๋ค. ํํธ, ์คํํธ๋ด ์ด์ค ๋ถํ ๋ฐฉ๋ฒ์ ํน์๊ฐ ๋ถํด๋ฅผ ๋ฐ๋ณตํ์ฌ ์ด๋ถ ๊ทธ๋ํ๋ฅผ ์ ๋๋ฉ์ธ์ ์ ๋ณด๋ฅผ ๋ชจ๋ ํฌํจํ ๋ถ๋ถ๊ทธ๋ํ๋ก ๋ถํ ํ๋ค. ์ถ์ฒ์์คํ
์ ์ํ ๋ฐ์ดํฐ ์ธํธ๋ฅผ ์คํํธ๋ด ์ด์ค ๋ถํ ํ ๊ฒฝ์ฐ, ํน์ ์ฌ์ฉ์๊ทธ๋ฃน๊ณผ ์์ดํ
๊ทธ๋ฃน์ ์ ์ฒด ๋ฐ์ดํฐ๋ก๋ถํฐ ๋ถ๋ฆฌํ ์ ์์ผ๋ฉฐ, ๋ถํ ๋ ๊ทธ๋ฃน์ ๋์ ๋ฐ์ดํฐ ๋ฐ๋์ ๊ฐํ ์ํธ์์ฉ ์ ํธ๋ฅผ ๊ฐ๊ฒ ๋๋ค. ๋ฐ๋ผ์ ๋ถํ ๋ ๊ทธ๋ฃน ๋ฐ์ดํฐ์ ๋ํด ํ์
ํํฐ๋ง์ ์ ์ฉํ ๊ฒฝ์ฐ, ๋ฐ์ดํฐ ์ธํธ๋ ํ์
ํํฐ๋ง ๋ชจ๋ธ์ ์ข
๋ฅ์ ๊ด๊ณ์์ด ํด๋น ๋ฐ์ดํฐ๊ทธ๋ฃน์์๋ ์ถ์ฒ ๋ฅ๋ ฅ์ด ํฅ์๋๋ค. ๋์๊ฐ์, ๋ถํ ๋ ๋ถ๋ถ ๊ทธ๋ํ๋ค์ ๊ฐ๋ณ์ ์ผ๋ก ํ์
ํํฐ๋งํ ๋ค ์์๋ธ ํ์ฌ ๊ทธ๋ฃน๋ณ ์ํธ์์ฉ ์ ํธ๋ฅผ ๋ถ์ํ ์ง์ญ์๋ฒ ๋ฉ(Local Embedding)๊ณผ ์ ์ฒด ๋ฐ์ดํฐ๋ฅผ ์์ฐ๋ฅผ ์ ์๋ ์ ์ญ์๋ฒ ๋ฉ(Global Embedding)์ ํตํฉํ์ฌ ์ต์ข
์ ๋ฒ ๋ฉ์ ๊ตฌ์ฑํ์๋ค. ์ฌ์ฏ ๊ฐ์ ๋ฐ์ดํฐ ์ธํธ์ ์ธ ๊ฐ์ง์ ํ์
ํํฐ๋ง ๋ชจ๋ธ์ ์คํํธ๋ด ์ด์ค ๋ถํ ํ์ฌ ์์๋ธ ํ ๊ฒฐ๊ณผ, ๋ชจ๋ธ ์ข
๋ฅ์ ๊ด๊ณ์์ด ์ถ์ฒ ๋ฅ๋ ฅ์ด ํฅ์๋์๋ค. ๊ทธ๋ฌ๋ ๋ช ๊ฐ์ง ๋ฐ์ดํฐ ์ธํธ์ ๊ฒฝ์ฐ ์ฑ๋ฅํฅ์์ด ๊ฑฐ์ ์ด๋ฃจ์ด์ง์ง ์์๋๋ฐ, ์ด๋ ๋ฐ์ดํฐ๊ฐ ์ด๋ฏธ ์ ์ ํ ๋ถํ ๋์ด์๋ ๊ฒฝ์ฐ ์คํํธ๋ด ์ด์ค ๋ถํ ์ด ์ถ์ฒ ์ฑ๋ฅ์ ํฅ์ํ์ง ๋ชปํ ๊ฒ์ผ๋ก ๋ถ์๋๋ค. ๋ฐ๋ฉด ๋ฐ์ดํฐ๊ฐ ๊ณจ๊ณ ๋ฃจ ๋ถํฌ๋์ด์์ด ๊ธฐ๋ณธ ํ์
ํํฐ๋ง ๋ชจ๋ธ์ด ์ํธ์์ฉ ์ ํธ๋ฅผ ๋ถ์ํ๊ธฐ ์ด๋ ค์ด ๊ฒฝ์ฐ, ์คํํธ๋ด ์ด์ค ๋ถํ ์ ํตํ ์์๋ธ ๋ฐฉ๋ฒ์ผ๋ก ๋ชจ๋ ํ์
ํํฐ๋ง ๋ชจ๋ธ์ ๋ํ์ฌ ์ถ์ฒ ๋ฅ๋ ฅ์ด ํฅ์๋์๋ค1 Introduction 1
2 Preliminaries 5
2.1 Spectral Co-Clustering 5
2.1.1 Bipartite Graph Partitioning 5
2.1.2 Optimization 8
2.2 Bayesian Personalized Ranking(BPR) Loss 11
2.2.1 Implicit Data 11
2.2.2 Personalized Total Ranking 11
2.2.3 Bayesian Personalized Ranking 12
3 Proposed Method 15
3.1 Dataset 15
3.2 Spectral Co-Clustering 15
3.3 Local-Ensemble model 17
4 Experimental Result 20
4.1 Evaluation Metric 20
4.2 Result Analysis 21
5 Conclusion 29
References 31
Abstract (in Korean) 35์
Enhancement of urban pluvial flood risk management and resilience through collaborative modelling: a UK case study
This paper presents the main findings and lessons learned from the development and implementation of a new methodology for collaborative modelling, social learning and social acceptance of flood risk management technologies. The proposed methodology entails three main phases: (1) stakeholder analysis and engagement; (2) improvement of urban pluvial flood modelling and forecasting tools; and (3) development and implementation of web-based tools for collaborative modelling in flood risk management and knowledge sharing. The developed methodology and tools were tested in the Cranbrook catchment (London Borough of Redbridge, UK), an area that has experienced severe pluvial (surface) flooding in the past. The developed methodologies proved to be useful for promoting interaction between stakeholders, developing collaborative modelling and achieving social acceptance of new technologies for flood risk management. Some limitations for stakeholder engagement were identified and are discussed in the present paper
Permutation Models for Collaborative Ranking
We study the problem of collaborative filtering where ranking information is
available. Focusing on the core of the collaborative ranking process, the user
and their community, we propose new models for representation of the underlying
permutations and prediction of ranks. The first approach is based on the
assumption that the user makes successive choice of items in a stage-wise
manner. In particular, we extend the Plackett-Luce model in two ways -
introducing parameter factoring to account for user-specific contribution, and
modelling the latent community in a generative setting. The second approach
relies on log-linear parameterisation, which relaxes the discrete-choice
assumption, but makes learning and inference much more involved. We propose
MCMC-based learning and inference methods and derive linear-time prediction
algorithms
A Harmonic Extension Approach for Collaborative Ranking
We present a new perspective on graph-based methods for collaborative ranking
for recommender systems. Unlike user-based or item-based methods that compute a
weighted average of ratings given by the nearest neighbors, or low-rank
approximation methods using convex optimization and the nuclear norm, we
formulate matrix completion as a series of semi-supervised learning problems,
and propagate the known ratings to the missing ones on the user-user or
item-item graph globally. The semi-supervised learning problems are expressed
as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and
can be discretized by a point integral method. We show that our approach does
not impose a low-rank Euclidean subspace on the data points, but instead
minimizes the dimension of the underlying manifold. Our method, named LDM (low
dimensional manifold), turns out to be particularly effective in generating
rankings of items, showing decent computational efficiency and robust ranking
quality compared to state-of-the-art methods
Neural Collaborative Ranking
Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And deep learning methods have been shown to achieve state-of-the-art
on many recommendation tasks. For example, a recent model, NeuMF, first
projects users and items into some shared low-dimensional latent feature space,
and then employs neural nets to model the interaction between the user and item
latent features to obtain state-of-the-art performance on the recommendation
tasks. NeuMF assumes that the non-interacted items are inherent negative and
uses negative sampling to relax this assumption. In this paper, we examine an
alternative approach which does not assume that the non-interacted items are
necessarily negative, just that they are less preferred than interacted items.
Specifically, we develop a new classification strategy based on the widely used
pairwise ranking assumption. We combine our classification strategy with the
recently proposed neural collaborative filtering framework, and propose a
general collaborative ranking framework called Neural Network based
Collaborative Ranking (NCR). We resort to a neural network architecture to
model a user's pairwise preference between items, with the belief that neural
network will effectively capture the latent structure of latent factors. The
experimental results on two real-world datasets show the superior performance
of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and
Knowledge Managemen
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