2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第8页

A System to Enforce User's Preference in OSN Advertising 一种在OSN广告中强制用户偏好的系统

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345618

G. Lax, A. Russo

引用次数: 1

Measuring the Sampling Robustness of Complex Networks 测量复杂网络的采样鲁棒性

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342873

K. Areekijseree, S. Soundarajan

{"title":"Measuring the Sampling Robustness of Complex Networks","authors":"K. Areekijseree, S. Soundarajan","doi":"10.1145/3341161.3342873","DOIUrl":"https://doi.org/10.1145/3341161.3342873","url":null,"abstract":"When studying a network, it is often of interest to understand the robustness of that network to noise. Network robustness has been studied in a variety of contexts, examining network properties such as the number of connected components and the lengths of shortest paths. In this work, we present a new network robustness measure, which we refer to as ‘sampling robustness'. The goal of the sampling robustness measure is to quantify the extent to which a network sample collected from a graph with errors is a good representation of a network sample collected from that same graph, but without errors. These errors may be introduced by humans or by the system (e.g., mistakes from the respondents or a bug in an API program), and may affect the performance of a data collection algorithm and the quality of the obtained sample. Thus, when data analysts analyze the sampled network, they may wish to know whether such errors will affect future analysis results. We demonstrate that sampling robustness is dependent on a few easily-computed properties of the network: the leading eigenvalue, average node degree and clustering coefficient. In addition, we introduce regression models for estimating sampling robustness given an obtained sample. As a result, our models can estimate the sampling robustness with MSE < 0.0015 and the model has an R-squared of up to 75%.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

TF-MF: Improving Multiview Representation for Twitter User Geolocation Prediction TF-MF:改进Twitter用户地理位置预测的多视图表示

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342961

P. Hamouni, Taraneh Khazaei, Ehsan Amjadian

引用次数: 8

Phrase-Guided Attention Web Article Recommendation for Next Clicks and Views 短语引导的注意力网络文章推荐下一步点击和视图

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342869

Chia-Wei Chen, Sheng-Chuan Chou, Chang-You Tai, Lun-Wei Ku

{"title":"Phrase-Guided Attention Web Article Recommendation for Next Clicks and Views","authors":"Chia-Wei Chen, Sheng-Chuan Chou, Chang-You Tai, Lun-Wei Ku","doi":"10.1145/3341161.3342869","DOIUrl":"https://doi.org/10.1145/3341161.3342869","url":null,"abstract":"As deep learning models are getting popular, upgrading the retrieval-based content recommendation system to the learning-based system is highly demanded. However, efficiency is a critical issue. For article recommendation, an effective neural network which generates a good representation of the article content could prove useful. Hence, we propose PGA-Recommender, a phrase-guided article recommendation model which mimics the process of human behavior - first browsing, then guided by key phrases, and finally aggregating the gleaned information. As this can be performed independently offline, it is thus compatible with current commercial retrieval-based (keyword-based) article recommender systems. A total of six months of real logs - from Apr 2017 to Sep 2017 - were used for experiments. Results show that PGA-Recommender outperforms different state-of-the-art schemes including session-, collaborative filter-, and content-based recommendation models. Moreover, it suggests a diverse mix of articles while maintaining superior performance in terms of both click and view predictions. The results of A/B tests show that simply using the backward version of PGA-Recommender yields 40% greater click-through rates as compared to the retrieval-based system when deployed to a language of which we have zero knowledge.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

When to Remember Where You Came from: Node Representation Learning in Higher-order Networks 何时记起你来自哪里:高阶网络中的节点表示学习

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342911

Caleb Belth, Fahad Kamran, Donna Tjandra, Danai Koutra

引用次数: 7

News Credibility Scroing: Suggestion of research methodology to determine the reliability of news distributed in SNS 新闻可信度评价:研究方法的建议，以确定新闻在社交媒体传播的可靠性

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343683

Ki-young Shin, Woosang Song, Jinhee Kim, Jong-Hyeok Lee

引用次数: 0

Effects of Ego Networks and Communities on Self-Disclosure in an Online Social Network 自我网络和社区对在线社交网络中自我表露的影响

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342881

Young D. Kwon, Reza Hadi Mogavi, E. Haq, Young D. Kwon, Xiaojuan Ma, Pan Hui

{"title":"Effects of Ego Networks and Communities on Self-Disclosure in an Online Social Network","authors":"Young D. Kwon, Reza Hadi Mogavi, E. Haq, Young D. Kwon, Xiaojuan Ma, Pan Hui","doi":"10.1145/3341161.3342881","DOIUrl":"https://doi.org/10.1145/3341161.3342881","url":null,"abstract":"Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level. In this paper, we conduct a quantitative study at different granularities of networks (ego networks and user communities) to understand users' self-disclosing behaviors better. As our first contribution, we characterize users into three types (open, closed, and moderate) based on the Communication Privacy Management theory and extend the analysis of the self-disclosure of users to a large-scale OSN dataset which could represent the entire network structure. As our second contribution, we show that our proposed features of ego networks and positional and structural properties of communities significantly affect self-disclosing behavior. Based on these insights, we present the possible relation between the propensity of the self-disclosure of users and the sociological theory of structural holes, i.e., users at a bridge position can leverage advantages among distinct groups. To the best of our knowledge, our study provides the first attempt to shed light on the self-disclosure of users using the whole network structure, which paves the way to a better understanding of users' self-disclosing behaviors and their relations with overall network structures.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Knowledge Embedding towards the Recommendation with Sparse User-Item Interactions 面向稀疏用户-项目交互推荐的知识嵌入

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342876

Deqing Yang, Ziyi Wang, Junyan Jiang, Yanghua Xiao

{"title":"Knowledge Embedding towards the Recommendation with Sparse User-Item Interactions","authors":"Deqing Yang, Ziyi Wang, Junyan Jiang, Yanghua Xiao","doi":"10.1145/3341161.3342876","DOIUrl":"https://doi.org/10.1145/3341161.3342876","url":null,"abstract":"Recently, many researchers in recommender systems have realized that encoding user-item interactions based on deep neural networks (DNNs) promotes collaborative-filtering (CF)'s performance. Nonetheless, those DNN-based models' performance is still limited when observed user-item interactions are very less because the training samples distilled from these interactions are critical for deep learning models. To address this problem, we resort to plenty features distilled from knowledge graphs (KGs), to profile users and items precisely and sufficiently rather than observed user-item interactions. In this paper, we propose a knowledge embedding based recommendation framework to alleviate the problem of sparse user-item interactions in recommendation. In our framework, each user and each item are both represented by the combination of an item embedding and a tag embedding at first. Specifically, item embeddings are learned by Metapath2Vec which is a graph embedding model qualified to embedding heterogeneous information networks. Tag embeddings are learned by a Skip-gram model similar to word embedding. We regarded these embeddings as knowledge embeddings because they both indicate knowledge about the latent relationships of movie-movie and user-movie. At last, a target user's representation and a candidate movie's representation are both fed into a multi-layer perceptron to output the probability that the user likes the item. The probability can be further used to achieve top-n recommendation. The extensive experiments on a movie recommendation dataset demonstrate our framework's superiority over some state-of-the-art recommendation models, especially in the scenario of sparse user-movie interactions.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115893116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Beauty lies in the face of the beholder: A Bi-channel CNN architecture for attractiveness modeling in matrimony 情人眼里出西施:一种用于婚姻中吸引力建模的双通道CNN架构

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345307

A. Saw, Nitendra Rajput

引用次数: 0

Measurement and Analysis of an Adult Video Streaming Service 成人视频流媒体服务的测量与分析

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342940

Yo-Der Song, Mingwei Gong, Aniket Mahanti

引用次数: 6