Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining最新文献_第4页

Beyond Who and What: Data Driven Approaches for User Characterization 超越谁和什么:用户特征的数据驱动方法

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3170455

Aastha Nigam

{"title":"Beyond Who and What: Data Driven Approaches for User Characterization","authors":"Aastha Nigam","doi":"10.1145/3159652.3170455","DOIUrl":"https://doi.org/10.1145/3159652.3170455","url":null,"abstract":"Social media and technology have drastically transformed the social and information networks around us. They have impacted how we communicate with others, search for information, and even how we express our personal opinions. Further, in this era of big data, not only are the online services collecting vast variety of user data, but we, as users, are also readily divulging significant amounts of information. Together, massive datasets obtained from diverse sources such as organizations and user generated content give us the opportunity to explore and understand complex behavior of both individuals and communities. This proposal aims at designing generalizable and scalable data-driven frameworks to gain a deeper understanding of the users, explain their actions and preferences, and infer personal traits. The proposed models will enable us to move beyond asking the conventional questions of who and what, and reveal answers about how and why. Given the varying digital persona of users motivated by their personal preferences and social attributes, we characterize users in two distinct domains: online health and peace studies. The models are designed to solve various real-world challenges to maximize their broader impact.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114888854","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: 0

Fusing Diversity in Recommendations in Heterogeneous Information Networks 异构信息网络中推荐多样性的融合

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159720

Sharad Nandanwar, Aayush Moroney, M. Murty

{"title":"Fusing Diversity in Recommendations in Heterogeneous Information Networks","authors":"Sharad Nandanwar, Aayush Moroney, M. Murty","doi":"10.1145/3159652.3159720","DOIUrl":"https://doi.org/10.1145/3159652.3159720","url":null,"abstract":"In the past, hybrid recommender systems have shown the power of exploiting relationships amongst objects which directly or indirectly effect the recommendation task. However, the effect of all relations is not equal, and choosing their right balance for a recommendation problem at hand is non-trivial. We model these interactions using a Heterogeneous Information Network, and propose a systematic framework for learning their influence weights for a given recommendation task. Further, we address the issue of redundant results, which is very much prevalent in recommender systems. To alleviate redundancy in recommendations we use Vertex Reinforced Random Walk (a non-Markovian random walk) over a heterogeneous graph. It works by boosting the transitions to the influential nodes, while simultaneously shrinking the weights of others. This helps in discouraging recommendation of multiple influential nodes which lie in close proximity of each other, thus ensuring diversity. Finally, we demonstrate the effectiveness of our approach by experimenting on real world datasets. We find that, with the weights of relations learned using the proposed non-Markovian random walk based framework, the results consistently improve over the baselines.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908984","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: 17

FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks FACH:大型网络中社区内聚层次的快速检测算法

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159704

Mojtaba Rezvani, Qing Wang, W. Liang

{"title":"FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks","authors":"Mojtaba Rezvani, Qing Wang, W. Liang","doi":"10.1145/3159652.3159704","DOIUrl":"https://doi.org/10.1145/3159652.3159704","url":null,"abstract":"Vertices in a real-world social network can be grouped into densely connected communities that are sparsely connected to other groups. Moreover, these communities can be partitioned into successively more cohesive communities. Despite an ever-growing pile of research on hierarchical community detection, existing methods suffer from either inefficiency or inappropriate modeling. Yet, some cut-based approaches have shown to be effective in finding communities without hierarchies. In this paper, we study the hierarchical community detection problem in large networks and show that it is NP-hard. We then propose an efficient algorithm based on edge-cuts to identify the hierarchy of communities. Since communities at lower levels of the hierarchy are denser than the higher levels, we leverage a fast network sparsification technique to enhance the running time of the algorithm. We further propose a randomized approximation algorithm for information centrality of networks. We finally evaluate the performance of the proposed algorithms by conducting extensive experiments using real datasets. Our experimental results show that the proposed algorithms are promising and outperform the state-of-the-art algorithms by several orders of magnitude.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036737","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: 1

Conversational Semantic Search: Looking Beyond Web Search, Q&A and Dialog Systems 会话语义搜索:超越网络搜索、问答和对话系统

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3160590

Paul A. Crook, Alex Marin, Vipul Agarwal, Samantha Anderson, Ohyoung Jang, Aliasgar Lanewala, K. Tangirala, I. Zitouni

引用次数: 11

Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration 短期满意度和长期覆盖:了解用户如何容忍算法探索

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159700

Tobias Schnabel, Paul N. Bennett, S. Dumais, T. Joachims

{"title":"Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration","authors":"Tobias Schnabel, Paul N. Bennett, S. Dumais, T. Joachims","doi":"10.1145/3159652.3159700","DOIUrl":"https://doi.org/10.1145/3159652.3159700","url":null,"abstract":"Any learning algorithm for recommendation faces a fundamental trade-off between exploiting partial knowledge of a user»s interests to maximize satisfaction in the short term and discovering additional user interests to maximize satisfaction in the long term. To enable discovery, a machine learning algorithm typically elicits feedback on items it is uncertain about, which is termed algorithmic exploration in machine learning. This exploration comes with a cost to the user, since the items an algorithm chooses for exploration frequently turn out to not match the user»s interests. In this paper, we study how users tolerate such exploration and how presentation strategies can mitigate the exploration cost. To this end, we conduct a behavioral study with over 600 people, where we vary how algorithmic exploration is mixed into the set of recommendations. We find that users respond non-linearly to the amount of exploration, where some exploration mixed into the set of recommendations has little effect on short-term satisfaction and behavior. For long-term satisfaction, the overall goal is to learn via exploration about the items presented. We therefore also analyze the quantity and quality of implicit feedback signals such as clicks and hovers, and how they vary with different amounts of mix-in exploration. Our findings provide insights into how to design presentation strategies for algorithmic exploration in interactive recommender systems, mitigating the short-term costs of algorithmic exploration while aiming to elicit informative feedback data for learning.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188490","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: 40

Inferring Dockless Shared Bike Distribution in New Cities 新城市无桩共享单车分布推断

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159708

Zhaoyang Liu, Yanyan Shen, Yanmin Zhu

引用次数: 39

A Discrete Choice Model for Subset Selection 子集选择的离散选择模型

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159702

Austin R. Benson, Ravi Kumar, A. Tomkins

{"title":"A Discrete Choice Model for Subset Selection","authors":"Austin R. Benson, Ravi Kumar, A. Tomkins","doi":"10.1145/3159652.3159702","DOIUrl":"https://doi.org/10.1145/3159652.3159702","url":null,"abstract":"Multinomial logistic regression is a classical technique for modeling how individuals choose an item from a finite set of alternatives. This methodology is a workhorse in both discrete choice theory and machine learning. However, it is unclear how to generalize multinomial logistic regression to subset selection, allowing the choice of more than one item at a time. We present a new model for subset selection derived from the perspective of random utility maximization in discrete choice theory. In our model, the quality of a subset is determined by the quality of its elements, plus an optional correction. Given a budget on the number of subsets that may receive correction, we develop a framework for learning the quality scores for each item, the choice of subsets, and the correction for each subset. We show that, given the subsets to receive correction, we can efficiently and optimally learn the remaining model parameters jointly. We show further that learning the optimal subsets is both NP-hard and non-submodular, but there are efficient heuristics that perform well in practice. We combine these pieces to provide an overall learning solution and apply it to subset prediction tasks. We find that with reasonably-sized budgets, there are significant gains in average per-choice likelihood ranging from 7% to 8x depending on the dataset and also substantial improvements over a determinantal point process model.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129445453","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: 40

Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs 真实世界图上快速可扩展的分布式环路信念传播

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3159722

Saehan Jo, Jaemin Yoo, U. Kang

{"title":"Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs","authors":"Saehan Jo, Jaemin Yoo, U. Kang","doi":"10.1145/3159652.3159722","DOIUrl":"https://doi.org/10.1145/3159652.3159722","url":null,"abstract":"Given graphs with millions or billions of vertices and edges, how can we efficiently make inferences based on partial knowledge? Loopy Belief Propagation(LBP) is a graph inference algorithm widely used in various applications including social network analysis, malware detection, recommendation, and image restoration. The algorithm calculates approximate marginal probabilities of vertices in a graph within a linear running time proportional to the number of edges. However, when it comes to real-world graphs with millions or billions of vertices and edges, this cost overwhelms the computing power of a single machine. Moreover, this kind of large-scale graphs does not fit into the memory of a single machine. Although several distributed LBP methods have been proposed, previous works do not consider the properties of real-world graphs, especially the effect of power-law degree distribution on LBP. Therefore, our work focuses on developing a fast and scalable LBP for such large real-world graphs on distributed environment. In this paper, we propose DLBP, a Distributed Loopy Belief Propagation algorithm which efficiently computes LBP in a distributed manner across multiple machines. By setting the correct convergence criterion and carefully scheduling the computations, DLBP provides up to 10.7x speed up compared to standard distributed LBP. We show that DLBP demonstrates near-linear scalability with respect to the number of machines as well as the number of edges.","PeriodicalId":401247,"journal":{"name":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129739410","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: 10

HeteroNAM: International Workshop on Heterogeneous Networks Analysis and Mining 异构网络分析与挖掘国际研讨会

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3160591

Shobeir Fakhraei, Yanen Li, Yizhou Sun, Tim Weninger

引用次数: 0

Workshop on Two-sided Marketplace Optimization: Search,Pricing, Matching & Growth 双边市场优化研讨会:搜索，定价，匹配和增长

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI: 10.1145/3159652.3160593

Mihajlo Grbovic, Thanasis Noulas

引用次数: 0