Identifying personalities accurately helps merchants and management departments understand user needs in detail and improve the quality of service and decision-making efficiency.Existing research on text-based persona...Identifying personalities accurately helps merchants and management departments understand user needs in detail and improve the quality of service and decision-making efficiency.Existing research on text-based personality prediction mainly uses deep neural networks or pretrained language models to mine deep semantics,ignoring the dynamic interactions among personality features.This paper presents a novel personality prediction method that simultaneously taps into the capability of graph neural networks to model the deep interactions among features and that of pretrained language models to learn latent semantics with a hierarchical aggregation mechanism.Specifically,the proposed model leverages self-attention to capture the interaction relationships among POS tags,entities,personality tags,etc.,and considers the labels’cooccurrence patterns.The efficacy of the proposed model is evaluated on the myPersonality and PANDORA datasets.This research contributes to the personality prediction literature from the perspective of a multigranular personality feature learning perspective and provides business value for consuming predictive analytics.展开更多
Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities be- tween individuals. Recently, we have witnessed a...Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities be- tween individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly em- pirical results. We then provide a concise description of some related social models from both macro- and micro-level per- spectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social sys- tems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.展开更多
Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities...Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.展开更多
Group behavior forecasting is an emergent re- search and application field in social computing. Most of the existing group behavior forecasting methods have heavily re- lied on structured data which is usually hard to...Group behavior forecasting is an emergent re- search and application field in social computing. Most of the existing group behavior forecasting methods have heavily re- lied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we pro- pose a computational approach based on the recognition of multiple plans/intentions underlying group behavior. We fur- ther conduct human experiment to empirically evaluate the effectiveness of our proposed approach.展开更多
The rapid increase of user-generated content （UGC） is a rich source for reputation management of enti- ties, products, and services. Looking at online product re- views as a concrete example, in reviews, customers u...The rapid increase of user-generated content （UGC） is a rich source for reputation management of enti- ties, products, and services. Looking at online product re- views as a concrete example, in reviews, customers usually give opinions on multiple attributes of products, therefore the challenge is to automatically extract and cluster attributes that are mentioned. In this paper, we investigate efficient at- tribute extraction models using a semi-supervised approach. Specifically, we formulate the attribute extraction issue as a sequence labeling task and design a bootstrapped schema to train the extraction models by leveraging a small quantity of labeled reviews and a larger number of unlabeled reviews. In addition, we propose a clustering By committee （CBC） ap- proach to cluster attributes according to their semantic simi- larity. Experimental results on real world datasets show that the proposed approach is effective.展开更多
Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing rank...Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.展开更多
基金supported by the National Natural Science Foundation of China(Nos.72293575,62071467 and 62141608).
文摘Identifying personalities accurately helps merchants and management departments understand user needs in detail and improve the quality of service and decision-making efficiency.Existing research on text-based personality prediction mainly uses deep neural networks or pretrained language models to mine deep semantics,ignoring the dynamic interactions among personality features.This paper presents a novel personality prediction method that simultaneously taps into the capability of graph neural networks to model the deep interactions among features and that of pretrained language models to learn latent semantics with a hierarchical aggregation mechanism.Specifically,the proposed model leverages self-attention to capture the interaction relationships among POS tags,entities,personality tags,etc.,and considers the labels’cooccurrence patterns.The efficacy of the proposed model is evaluated on the myPersonality and PANDORA datasets.This research contributes to the personality prediction literature from the perspective of a multigranular personality feature learning perspective and provides business value for consuming predictive analytics.
文摘Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities be- tween individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly em- pirical results. We then provide a concise description of some related social models from both macro- and micro-level per- spectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social sys- tems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.
基金supported in part by the National Natural Science Foundation of China (90924302, 91024030, 71025001, 70890084, and 60921061)the US Defense Advanced Research Projects through two seedling grants to Rensselaer Polytechnic Institutethe US National Science Foundation support for EAGER (IIS-1143585)
文摘Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.
文摘Group behavior forecasting is an emergent re- search and application field in social computing. Most of the existing group behavior forecasting methods have heavily re- lied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we pro- pose a computational approach based on the recognition of multiple plans/intentions underlying group behavior. We fur- ther conduct human experiment to empirically evaluate the effectiveness of our proposed approach.
文摘The rapid increase of user-generated content （UGC） is a rich source for reputation management of enti- ties, products, and services. Looking at online product re- views as a concrete example, in reviews, customers usually give opinions on multiple attributes of products, therefore the challenge is to automatically extract and cluster attributes that are mentioned. In this paper, we investigate efficient at- tribute extraction models using a semi-supervised approach. Specifically, we formulate the attribute extraction issue as a sequence labeling task and design a bootstrapped schema to train the extraction models by leveraging a small quantity of labeled reviews and a larger number of unlabeled reviews. In addition, we propose a clustering By committee （CBC） ap- proach to cluster attributes according to their semantic simi- larity. Experimental results on real world datasets show that the proposed approach is effective.
文摘Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.
