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    ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๊ตฐ์ง‘ํ™”๋ฅผ ์ด์šฉํ•œ ๊ทธ๋ž˜ํ”„๊ธฐ๋ฐ˜ ํ˜‘์—…ํ•„ํ„ฐ๋ง์˜ ๊ตญ์ง€ ์•™์ƒ๋ธ” ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ณ„์‚ฐ๊ณผํ•™์ „๊ณต, 2022. 8. ๊ฐ•๋ช…์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 ๋…ผ๋ฌธ์—์„œ๋Š” ์ถ”์ฒœ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ํ˜‘์—… ํ•„ํ„ฐ๋ง ๋ชจ๋ธ์„ ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๋ถ„ํ• ํ•˜์—ฌ ์ƒ์„ฑ๋œ ๋ถ€๋ถ„ ๊ทธ๋ž˜ํ”„๋ฅผ ์•™์ƒ๋ธ”(Ensemble) ํ•˜์—ฌ ์ถ”์ฒœ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ทธ๋ž˜ํ”„ ์ธ๊ณต ์‹ ๊ฒฝ๋ง (Graph Neural Networks, GNN)์„ ์ด์šฉํ•œ ํ˜‘์—… ํ•„ํ„ฐ๋ง ๊ธฐ๋ฐ˜์˜ ์ถ”์ฒœ ์‹œ์Šคํ…œ์˜ ๊ธฐ๋ณธ ๋ชจ๋ธ์€, ์‚ฌ์šฉ์ž๋‚˜ ์•„์ดํ…œ์— ๋Œ€ํ•œ ์‚ฌ์ „ ์ •๋ณด๋ฅผ ์ „ํ˜€ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์‚ฌ์šฉ์ž-์•„์ดํ…œ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ ์ •๋ณด๋งŒ์„ ํ™œ์šฉํ•˜์—ฌ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์˜ ์ž„๋ฒ ๋”ฉ์„ ๊ตฌ์„ฑํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์‚ฌ์šฉ์ž์™€ ์•„์ดํ…œ์˜ ์‚ฌ์ „์ •๋ณด๋งŒ์œผ๋กœ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ๋Š” ํŠน์ • ์‚ฌ์šฉ์ž ๊ทธ๋ฃน์˜ ๊ฒฝํ–ฅ์„ฑ์„ ์ถ”์ฒœ์‹œ์Šคํ…œ์— ์‚ฌ์šฉํ•  ์ˆ˜ ์—†๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ํ•œํŽธ, ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๋ถ„ํ•  ๋ฐฉ๋ฒ•์€ ํŠน์ž‡๊ฐ’ ๋ถ„ํ•ด๋ฅผ ๋ฐ˜๋ณตํ•˜์—ฌ ์ด๋ถ„ ๊ทธ๋ž˜ํ”„๋ฅผ ์–‘ ๋„๋ฉ”์ธ์˜ ์ •๋ณด๋ฅผ ๋ชจ๋‘ ํฌํ•จํ•œ ๋ถ€๋ถ„๊ทธ๋ž˜ํ”„๋กœ ๋ถ„ํ• ํ•œ๋‹ค. ์ถ”์ฒœ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๋ถ„ํ•  ํ•  ๊ฒฝ์šฐ, ํŠน์ • ์‚ฌ์šฉ์ž๊ทธ๋ฃน๊ณผ ์•„์ดํ…œ ๊ทธ๋ฃน์„ ์ „์ฒด ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ถ„ํ• ๋œ ๊ทธ๋ฃน์€ ๋†’์€ ๋ฐ์ดํ„ฐ ๋ฐ€๋„์™€ ๊ฐ•ํ•œ ์ƒํ˜ธ์ž‘์šฉ ์‹ ํ˜ธ๋ฅผ ๊ฐ–๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ถ„ํ• ๋œ ๊ทธ๋ฃน ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ํ˜‘์—… ํ•„ํ„ฐ๋ง์„ ์ ์šฉํ•  ๊ฒฝ์šฐ, ๋ฐ์ดํ„ฐ ์„ธํŠธ๋‚˜ ํ˜‘์—… ํ•„ํ„ฐ๋ง ๋ชจ๋ธ์˜ ์ข…๋ฅ˜์™€ ๊ด€๊ณ„์—†์ด ํ•ด๋‹น ๋ฐ์ดํ„ฐ๊ทธ๋ฃน์—์„œ๋Š” ์ถ”์ฒœ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋œ๋‹ค. ๋‚˜์•„๊ฐ€์„œ, ๋ถ„ํ• ๋œ ๋ถ€๋ถ„ ๊ทธ๋ž˜ํ”„๋“ค์„ ๊ฐœ๋ณ„์ ์œผ๋กœ ํ˜‘์—… ํ•„ํ„ฐ๋งํ•œ ๋’ค ์•™์ƒ๋ธ” ํ•˜์—ฌ ๊ทธ๋ฃน๋ณ„ ์ƒํ˜ธ์ž‘์šฉ ์‹ ํ˜ธ๋ฅผ ๋ถ„์„ํ•œ ์ง€์—ญ์ž„๋ฒ ๋”ฉ(Local Embedding)๊ณผ ์ „์ฒด ๋ฐ์ดํ„ฐ๋ฅผ ์•„์šฐ๋ฅผ ์ˆ˜ ์žˆ๋Š” ์ „์—ญ์ž„๋ฒ ๋”ฉ(Global Embedding)์„ ํ†ตํ•ฉํ•˜์—ฌ ์ตœ์ข…์ž„ ๋ฒ ๋”ฉ์„ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์—ฌ์„ฏ ๊ฐœ์˜ ๋ฐ์ดํ„ฐ ์„ธํŠธ์™€ ์„ธ ๊ฐ€์ง€์˜ ํ˜‘์—… ํ•„ํ„ฐ๋ง ๋ชจ๋ธ์„ ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๋ถ„ํ• ํ•˜์—ฌ ์•™์ƒ๋ธ” ํ•œ ๊ฒฐ๊ณผ, ๋ชจ๋ธ ์ข…๋ฅ˜์™€ ๊ด€๊ณ„์—†์ด ์ถ”์ฒœ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ช‡ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๊ฒฝ์šฐ ์„ฑ๋Šฅํ–ฅ์ƒ์ด ๊ฑฐ์˜ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋Š”๋ฐ, ์ด๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ์ด๋ฏธ ์ ์ ˆํžˆ ๋ถ„ํ• ๋˜์–ด์žˆ๋Š” ๊ฒฝ์šฐ ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค‘ ๋ถ„ํ• ์ด ์ถ”์ฒœ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒํ•˜์ง€ ๋ชปํ•œ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋œ๋‹ค. ๋ฐ˜๋ฉด ๋ฐ์ดํ„ฐ๊ฐ€ ๊ณจ๊ณ ๋ฃจ ๋ถ„ํฌ๋˜์–ด์žˆ์–ด ๊ธฐ๋ณธ ํ˜‘์—… ํ•„ํ„ฐ๋ง ๋ชจ๋ธ์ด ์ƒํ˜ธ์ž‘์šฉ ์‹ ํ˜ธ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒฝ์šฐ, ์ŠคํŽ™ํŠธ๋Ÿด ์ด์ค€ ๋ถ„ํ• ์„ ํ†ตํ•œ ์•™์ƒ๋ธ” ๋ฐฉ๋ฒ•์œผ๋กœ ๋ชจ๋“  ํ˜‘์—… ํ•„ํ„ฐ๋ง ๋ชจ๋ธ์— ๋Œ€ํ•˜์—ฌ ์ถ”์ฒœ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋˜์—ˆ๋‹ค1 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

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    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

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    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

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    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

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    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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