26 research outputs found

    A Survey of Graph Neural Networks for Social Recommender Systems

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    Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.Comment: GitHub repository with the curated list of papers: https://github.com/claws-lab/awesome-GNN-social-recsy

    Temporal models for mining, ranking and recommendation in the Web

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    Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time

    Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

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    Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

    Modeling Time-Series and Spatial Data for Recommendations and Other Applications

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    With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.Comment: Ph.D. Thesis (2022

    Learning Representations of Social Media Users

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    User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

    Learning Representations of Social Media Users

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    User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi
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