Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

소셜 네트워크와 이커머스 플랫폼에서의 잠재 네트워크 마이닝

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2023. 2. 권태경.웹 기반 서비스의 폭발적인 발달로 사용자들은 온라인 상에서 폭넓게 연결되고 있다. 온라인 플랫폼 상에서, 사용자들은 서로에게 영향을 주고받으며 의사 결정에 그들의 경험과 의견을 반영하는 경향을 보인다. 본 학위 논문에서는 대표적인 온라인 플랫폼인 소셜 네트워크 서비스와 이커머스 플랫폼에서의 사용자 행동에 대해 연구하였다. 온라인 플랫폼에서의 사용자 행동은 사용자와 플랫폼 구성 요소 간의 관계로 표현할 수 있다. 사용자의 구매는 사용자와 상품 간의 관계로, 사용자의 체크인은 사용자와 장소 간의 관계로 나타내진다. 여기에 행동의 시간과 레이팅, 태그 등의 정보가 포함될 수 있다. 본 연구에서는 두 플랫폼에서 정의된 사용자의 행동 그래프에 영향을 미치는 잠재 네트워크를 파악하는 연구를 제시한다. 위치 기반의 소셜 네트워크 서비스의 경우 특정 장소에 방문하는 체크인 형식으로 많은 포스트가 만들어지는데, 사용자의 장소 방문은 사용자 간에 사전에 존재하는 친구 관계에 의해 영향을 크게 받는다. 사용자 활동 네트워크의 저변에 잠재된 사용자 간의 관계를 파악하는 것은 활동 예측에 도움이 될 수 있으며, 이를 위해 본 논문에서는 비지도학습 기반으로 활동 네트워크로부터 사용자 간 사회적 관계를 추출하는 연구를 제안하였다. 기존에 연구되었던 방법들은 두 사용자가 동시에 방문하는 행위인 co-visitation을 중점적으로 고려하여 사용자 간의 관계를 예측하거나, 네트워크 임베딩 또는 그래프 신경망(GNN)을 사용하여 표현 학습을 수행하였다. 그러나 이러한 접근 방식은 주기적인 방문이나 장거리 이동 등으로 대표되는 사용자의 행동 패턴을 잘 포착하지 못한다. 행동 패턴을 더 잘 학습하기 위해, ANES는 사용자 컨텍스트 내에서 사용자와 관심 지점(POI) 간의 측면(Aspect) 지향 관계를 학습한다. ANES는 User-POI 이분 그래프의 구조에서 사용자의 행동을 여러 개의 측면으로 나누고, 각각의 관계를 고려하여 행동 패턴을 추출하는 최초의 비지도학습 기반 접근 방식이다. 실제 LBSN 데이터에서 수행된 광범위한 실험에서, ANES는 기존에 제안되었던 기법들보다 높은 성능을 보여준다. 위치 기반 소셜 네트워크와는 다르게, 이커머스의 리뷰 시스템에서는 사용자들이 능동적인 팔로우/팔로잉 등의 행위를 수행하지 않고도 플랫폼에 의해 서로의 정보를 주고받고 영향력을 행사하게 된다. 이와 같은 사용자들의 행동 특성은 리뷰 스팸에 의해 쉽게 악용될 수 있다. 리뷰 스팸은 실제 사용자의 의견을 숨기고 평점을 조작하여 잘못된 정보를 전달하는 방식으로 이루어진다. 나는 이를 해결하기 위해 사용자 리뷰 데이터에서 사용자 간 사전 공모성(Collusiveness)의 가능성을 찾고, 이를 스팸 탐지에 활용한 방법인 SC-Com을 제안한다. SC-Com은 행동의 공모성으로부터 사용자 간 공모 점수를 계산하고 해당 점수를 바탕으로 전체 사용자를 유사한 사용자들의 커뮤니티로 분류한다. 그 후 스팸 유저와 일반 유저를 구별하는 데에 중요한 그래프 기반의 특징을 추출하여 감독 학습 기반의 분류기의 입력 데이터로 활용하는 방법을 제시한다. SC-Com은 공모성을 갖는 스팸 유저의 집합을 효과적으로 탐지한다. 실제 데이터셋을 이용한 실험에서, SC-Com은 기존 논문들 대비 스팸 탐지에 뛰어난 성능을 보여주었다. 위 논문에서 다양한 데이터에 대해 연구된 암시적 연결망 탐지 모델은 레이블이 없는 데이터에 대해서도 사전에 연결되었을 가능성이 높은 사용자들을 예측하므로, 실시간 위치 데이터나, 앱 사용 데이터 등의 다양한 데이터에서 활용할 수 있는 유용한 정보를 제공하여 광고 추천 시스템이나, 악성 유저 탐지 등의 분야에서 기여할 수 있을 것으로 기대한다.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63박

Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work

"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams

The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social Computing co-located with ICDM, 2018 Singapor

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