A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations

Distance-based Attention GNN for Node Embedding on Spatial Networks

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2021.8. 유기윤.공간 네트워크는 노드와 엣지가 거리 공간에 속하는 그래프 자료구조로 지표면 상의 여러 현상과 문제를 표현할 수 있다. 최근 딥러닝 기술의 급격한 발달과 더불어 그래프를 학습하는 다양한 딥러닝 모델들이 연구되고 있지만 노드 사이의 거리 요소를 고려하지 않아 공간 네트워크가 가진 거리 정보를 활용하지 못한다. 본 연구는 공간 네트워크에 속하는 거리 특성을 가진 노드들의 이웃 관계에서의 영향력을 알 수 있는 거리 기반 어텐션 기법을 소개하고 이를 활용한 그래프 딥러닝 모델인 DWGNN을 제안한다. DWGNN은 임베딩 과정 중 대상 노드에게 더욱 가까운 이웃 노드의 영향력을 상대적으로 멀리 위치한 이웃 노드들의 영향력보다 크게 주어 공간 네트워크 상 각 노드의 특징 추출을 효과적으로 수행한다. 실세계 노드 임베딩 문제에 대한 제안한 기법의 다양한 적용을 위하여 실내, 실외 공간 네트워크 노드 임베딩 실험을 진행했다. 기존에 정립된 GNN 모델과 제안한 기법을 활용한 모델을 비교해본 결과 노드 분류와 특성 벡터 추출에 더욱 뛰어난 성능을 보이는 것을 확인했다. 제안한 모델을 목적에 맞는 다양한 딥러닝 아키텍처 내 하나의 요소로서 개발했으며 공간 네트워크를 대상으로 한 다양한 분석을 위한 모델로 사용할 수 있을 것으로 기대한다.A spatial network is a graph data structure consisting of nodes and edges in a metric space, used to represent various real-world problems and phenomena. Recently, along with the rapid development of deep learning techniques, various Neural Network models for graphs have been studied. However, these models barely utilize distance information as they did not consider the distance factors between nodes present in spatial networks. This paper introduces the distance-based attention mechanism, which can measure the attention values from the relationship among the nodes with distance features in a spatial network, and proposes Distance-Weighted Graph Neural Networks(DWGNN). DWGNN effectively extracts features of each node in a spatial network by making the nearby neighboring nodes more influential to the target node than the distant neighboring nodes in the embedding update process. Two node embedding experiments with indoor and outdoor spatial networks have been conducted, to apply the proposed GNN model to real-world node embedding problems in spatial networks. Compared to the existing GNN models, DWGNN shows performance improvement in node classification tasks and extracting latent feature vectors. The suggested model is developed as an element for various GNN architecture depending on the purpose and is expected to be used as a model for analyzing various problems on spatial networks.1. 서 론 1 1.1. 연구 배경 및 목적 1 1.2. 관련 연구 4 1.2.1. 네트워크 임베딩 5 1.2.2. Graph Neural Networks 10 1.2.2.1. GNN의 기본 정의와 Deep Graph Encoder로서의 기능 수행 10 1.2.2.2. GNN 활용 모델 14 1.2.3. 엣지 속성을 고려한 노드 임베딩 모델 16 2. 연구 방법 19 2.1. 문제 정의 19 2.2. 거리 기반 네트워크 어텐션 메커니즘 22 2.3. Distance-Weighted Graph Neural Networks 28 2.3.1. 거리 기반 Graph ConvNet 28 2.3.2. DWGNN 모델 아키텍처 30 3. 실험 적용 및 결과 33 3.1. 결과 비교를 위한 GNN 모델 정의 33 3.2. 실내 네트워크: 도면 요소 추출 및 분류 35 3.2.1. 데이터 처리 및 환경 37 3.2.2. 모델 구성 및 학습 39 3.2.3. 실험 결과 분석 41 3.3. 실외 네트워크: 버스 네트워크 정류장 임베딩 48 3.3.1. 데이터 처리 및 환경 51 3.3.2. 모델 구성 및 학습 53 3.3.3. 실험 결과 분석 55 4. 결 론 59석

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida