Online platforms such as LinkedIn or specialized platforms such as Glassdoor are widely used by job seekers before applying for the job. These web platforms have rating and reviews about employer and jobs. Hence a job seeker do online search for the employer, before applying for the job. They try to find if the employer and job is good for them or not, what are the pros and cons of working there etc. Therefore, these reviews and ratings have an impact on job seekers decision as it portrays the pros and cons of working in a particular firm. Hence, the main objective of this study is main objective of this study is to find how the job seekers search for online employer reviews and the impact of these reviews on employer attractiveness and job pursuit intention. The other objective is to find the most crucial job factors that are given priority by the employee. For this, the study is proposed to be conducted in two stages, first, collecting data from the website Glassdoor, having 600000 companies' reviews. In the second stage, conducting an experimental study to examine the influence of job attributes (high vs. low) and employer rating (high vs. low) on job choice and employer attractiveness.
{"title":"Impact of Online Job Search and Job Reviews on Job Decision","authors":"Faiz Ahamad","doi":"10.1145/3336191.3372184","DOIUrl":"https://doi.org/10.1145/3336191.3372184","url":null,"abstract":"Online platforms such as LinkedIn or specialized platforms such as Glassdoor are widely used by job seekers before applying for the job. These web platforms have rating and reviews about employer and jobs. Hence a job seeker do online search for the employer, before applying for the job. They try to find if the employer and job is good for them or not, what are the pros and cons of working there etc. Therefore, these reviews and ratings have an impact on job seekers decision as it portrays the pros and cons of working in a particular firm. Hence, the main objective of this study is main objective of this study is to find how the job seekers search for online employer reviews and the impact of these reviews on employer attractiveness and job pursuit intention. The other objective is to find the most crucial job factors that are given priority by the employee. For this, the study is proposed to be conducted in two stages, first, collecting data from the website Glassdoor, having 600000 companies' reviews. In the second stage, conducting an experimental study to examine the influence of job attributes (high vs. low) and employer rating (high vs. low) on job choice and employer attractiveness.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128397712","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}
Natalia Silberstein, O. Somekh, Yair Koren, M. Aharon, Dror Porat, Avi Shahar, Tingyi Wu
Verizon Media native advertising (also known as Yahoo Gemini native) serves billions of ad impressions daily, reaching several hundreds of millions USD in revenue yearly. Although we strive to provide the best experience for our users, there will always be some users that dislike our ads in certain cases. To address these situations Gemini native platform provides an ad close mechanism that enables users to close ads that they dislike and also to provide a reasoning for their action. Surprisingly, users do care about their ad experience and their engagement with the ad close mechanism is quite significant. While the ad close rate (ACR) is lower than the click through rate (CTR), they are of the same order of magnitude, especially on Yahoo mail properties. Since ad close events indicate bad user experience caused mostly by poor ad quality, we would like to exploit the ad close signals to improve user experience and reduce the number of ad close events while maintaining a predefined total revenue loss. In this work we present our ad close mitigation (ACM) solution that penalizes ads with high closing likelihood, in our auctions. In particular, we use the ad close signal and other available features to predict the probability of an ad close event, and calculate the expected loss due to such event for using the true expected revenue in the auction. We show that this approach fundamentally changes the generalized second price (GSP) auction and provides incentive for advertisers to improve their ads' quality. Our solution was tested in both offline and large scale online settings, serving real Gemini native traffic. Results of the online experiment show that we are able to reduce the number of ad close events by more than 20%, while decreasing the revenue in less than 0.4%. In addition, we present a large scale analysis of the ad close signal that supports various design decisions and sheds light on ways the ad close mechanism affects different crowds.
{"title":"Ad Close Mitigation for Improved User Experience in Native Advertisements","authors":"Natalia Silberstein, O. Somekh, Yair Koren, M. Aharon, Dror Porat, Avi Shahar, Tingyi Wu","doi":"10.1145/3336191.3371798","DOIUrl":"https://doi.org/10.1145/3336191.3371798","url":null,"abstract":"Verizon Media native advertising (also known as Yahoo Gemini native) serves billions of ad impressions daily, reaching several hundreds of millions USD in revenue yearly. Although we strive to provide the best experience for our users, there will always be some users that dislike our ads in certain cases. To address these situations Gemini native platform provides an ad close mechanism that enables users to close ads that they dislike and also to provide a reasoning for their action. Surprisingly, users do care about their ad experience and their engagement with the ad close mechanism is quite significant. While the ad close rate (ACR) is lower than the click through rate (CTR), they are of the same order of magnitude, especially on Yahoo mail properties. Since ad close events indicate bad user experience caused mostly by poor ad quality, we would like to exploit the ad close signals to improve user experience and reduce the number of ad close events while maintaining a predefined total revenue loss. In this work we present our ad close mitigation (ACM) solution that penalizes ads with high closing likelihood, in our auctions. In particular, we use the ad close signal and other available features to predict the probability of an ad close event, and calculate the expected loss due to such event for using the true expected revenue in the auction. We show that this approach fundamentally changes the generalized second price (GSP) auction and provides incentive for advertisers to improve their ads' quality. Our solution was tested in both offline and large scale online settings, serving real Gemini native traffic. Results of the online experiment show that we are able to reduce the number of ad close events by more than 20%, while decreasing the revenue in less than 0.4%. In addition, we present a large scale analysis of the ad close signal that supports various design decisions and sheds light on ways the ad close mechanism affects different crowds.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131009432","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}
Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models.
{"title":"Learning a Joint Search and Recommendation Model from User-Item Interactions","authors":"Hamed Zamani","doi":"10.1145/3336191.3371818","DOIUrl":"https://doi.org/10.1145/3336191.3371818","url":null,"abstract":"Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043342","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}
S. Bandyopadhyay, N. Lokesh, Saley Vishal Vivek, M. Murty
Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.
{"title":"Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding","authors":"S. Bandyopadhyay, N. Lokesh, Saley Vishal Vivek, M. Murty","doi":"10.1145/3336191.3371788","DOIUrl":"https://doi.org/10.1145/3336191.3371788","url":null,"abstract":"Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133878189","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}
Jie Ma, Jun Liu, Yufei Li, Xin Hu, Yudai Pan, Shen Sun, Qika Lin
Coreference resolution aims at recognizing different forms in a document which refer to the same entity in the real world. Although many models have been proposed and achieved success, there still exist some challenges. Recent models that use recurrent neural networks to obtain mention representations ignore dependencies between spans and their proceeding distant spans, which will lead to predicted clusters that are locally consistent but globally inconsistent. In addition, these models are trained only by maximizing the marginal likelihood of gold antecedent spans from coreference clusters, which will make some gold mentions undetectable and cause unsatisfactory coreference results. To address these challenges, we propose a neural coreference resolution model. It employs mutual attention to take into account the dependencies between spans and their proceeding spans directly (use attention mechanism to capture global information between spans and their proceeding spans). And our model is trained by jointly optimizing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-2012 English dataset show that our model can detect the most gold mentions and achieve the state-of-the-art coreference performance compared with baselines.
{"title":"Jointly Optimized Neural Coreference Resolution with Mutual Attention","authors":"Jie Ma, Jun Liu, Yufei Li, Xin Hu, Yudai Pan, Shen Sun, Qika Lin","doi":"10.1145/3336191.3371787","DOIUrl":"https://doi.org/10.1145/3336191.3371787","url":null,"abstract":"Coreference resolution aims at recognizing different forms in a document which refer to the same entity in the real world. Although many models have been proposed and achieved success, there still exist some challenges. Recent models that use recurrent neural networks to obtain mention representations ignore dependencies between spans and their proceeding distant spans, which will lead to predicted clusters that are locally consistent but globally inconsistent. In addition, these models are trained only by maximizing the marginal likelihood of gold antecedent spans from coreference clusters, which will make some gold mentions undetectable and cause unsatisfactory coreference results. To address these challenges, we propose a neural coreference resolution model. It employs mutual attention to take into account the dependencies between spans and their proceeding spans directly (use attention mechanism to capture global information between spans and their proceeding spans). And our model is trained by jointly optimizing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-2012 English dataset show that our model can detect the most gold mentions and achieve the state-of-the-art coreference performance compared with baselines.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326785","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}
The word "deep learning" is generally regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that deep learning is not necessarily to be realized by neural networks and differentiable modules. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation or gradient-based adjustment. We will also talk about some recent advances and challenges in this direction of research.
{"title":"Can Deep Learning Only Be Neural Networks?","authors":"Zhi-Hua Zhou","doi":"10.1145/3336191.3372190","DOIUrl":"https://doi.org/10.1145/3336191.3372190","url":null,"abstract":"The word \"deep learning\" is generally regarded as a synonym of \"deep neural networks (DNNs)\". In this talk, we will discuss on essentials in deep learning and claim that deep learning is not necessarily to be realized by neural networks and differentiable modules. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation or gradient-based adjustment. We will also talk about some recent advances and challenges in this direction of research.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123110548","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}
For random walks on a graph, the mean hitting time $H_j$ from a vertex i chosen from the stationary distribution to the target vertex j can be used as a measure of importance for vertex j, while the Kemeny constant K is the mean hitting time from a vertex i to a vertex j selected randomly according to the stationary distribution. Both quantities have found a large variety of applications in different areas. However, their high computational complexity limits their applications, especially for large networks with millions of vertices. In this paper, we first establish a connection between the two quantities, representing K in terms of $H_j$ for all vertices. We then express both quantities in terms of quadratic forms of the pseudoinverse for graph Laplacian, based on which we develop an efficient algorithm that provides an approximation of $H_j$ for all vertices and K in nearly linear time with respect to the edge number, with high probability. Extensive experiment results on real-life and model networks validate both the efficiency and accuracy of the proposed algorithm.
{"title":"Nearly Linear Time Algorithm for Mean Hitting Times of Random Walks on a Graph","authors":"Zuobai Zhang, Wanyue Xu, Zhongzhi Zhang","doi":"10.1145/3336191.3371777","DOIUrl":"https://doi.org/10.1145/3336191.3371777","url":null,"abstract":"For random walks on a graph, the mean hitting time $H_j$ from a vertex i chosen from the stationary distribution to the target vertex j can be used as a measure of importance for vertex j, while the Kemeny constant K is the mean hitting time from a vertex i to a vertex j selected randomly according to the stationary distribution. Both quantities have found a large variety of applications in different areas. However, their high computational complexity limits their applications, especially for large networks with millions of vertices. In this paper, we first establish a connection between the two quantities, representing K in terms of $H_j$ for all vertices. We then express both quantities in terms of quadratic forms of the pseudoinverse for graph Laplacian, based on which we develop an efficient algorithm that provides an approximation of $H_j$ for all vertices and K in nearly linear time with respect to the edge number, with high probability. Extensive experiment results on real-life and model networks validate both the efficiency and accuracy of the proposed algorithm.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117154943","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}
The challenge of building a good recommendation system is deeply connected to missing data---unknown features and labels to suggest the most "valuable" items to the user. The mysterious properties of the power law distributions that generally arises out of recommender (and social systems in general) create skewed and long-tailed consumption patterns that are often still puzzling to many of us. Missing data and skewed distributions create not just accuracy and recall problems, but also capacity allocation problems, which are at the roots of recent debate on inclusiveness and responsibility. So how do we move forward in the face of these immense conceptual and practical issues? In our work, we have been asking ourselves ways to deriving insights from first principles and drawing inspiration from fields like statistical physics. Surprised, one might ask---what does the field of physics has to do with missing data in ranking and recommendations? As we all know, in the field of information systems, concepts like information entropy and probability have a rich intellectual history. This history is deeply connected to the greatest discoveries of science in the 19th century---statistical mechanics, thermodynamics, and specific concepts like thermal equilibrium. In this talk, I will take us on a journey connecting Boltzmann distribution and partition functions from statistical mechanics with importance weighting for learning better softmax functions, and then further to reinforcement learning, where we can plan better explorations using off-policy correction with policy gradient approaches. As I shall show, these techniques enable us to reason about missing data features, labels, and time dynamic patterns from our data.
{"title":"From Missing Data to Boltzmann Distributions and Time Dynamics: The Statistical Physics of Recommendation","authors":"Ed H. Chi","doi":"10.1145/3336191.3372193","DOIUrl":"https://doi.org/10.1145/3336191.3372193","url":null,"abstract":"The challenge of building a good recommendation system is deeply connected to missing data---unknown features and labels to suggest the most \"valuable\" items to the user. The mysterious properties of the power law distributions that generally arises out of recommender (and social systems in general) create skewed and long-tailed consumption patterns that are often still puzzling to many of us. Missing data and skewed distributions create not just accuracy and recall problems, but also capacity allocation problems, which are at the roots of recent debate on inclusiveness and responsibility. So how do we move forward in the face of these immense conceptual and practical issues? In our work, we have been asking ourselves ways to deriving insights from first principles and drawing inspiration from fields like statistical physics. Surprised, one might ask---what does the field of physics has to do with missing data in ranking and recommendations? As we all know, in the field of information systems, concepts like information entropy and probability have a rich intellectual history. This history is deeply connected to the greatest discoveries of science in the 19th century---statistical mechanics, thermodynamics, and specific concepts like thermal equilibrium. In this talk, I will take us on a journey connecting Boltzmann distribution and partition functions from statistical mechanics with importance weighting for learning better softmax functions, and then further to reinforcement learning, where we can plan better explorations using off-policy correction with policy gradient approaches. As I shall show, these techniques enable us to reason about missing data features, labels, and time dynamic patterns from our data.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122452339","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}
Question routing (QR) aims at recommending newly posted questions to the potential answerers who are most likely to answer the questions. The existing approaches that learn users' expertise from their past question-answering activities usually suffer from challenges in two aspects: 1) multi-faceted expertise and 2) temporal dynamics in the answering behavior. This paper proposes a novel temporal context-aware model in multiple granularities of temporal dynamics that concurrently address the above challenges. Specifically, the temporal context-aware attention characterizes the answerer's multi-faceted expertise in terms of the questions' semantic and temporal information simultaneously. Moreover, the design of the multi-shift and multi-resolution module enables our model to handle temporal impact on different time granularities. Extensive experiments on six datasets from different domains demonstrate that the proposed model significantly outperforms competitive baseline models.
{"title":"Temporal Context-Aware Representation Learning for Question Routing","authors":"Xuchao Zhang, Wei Cheng, Bo Zong, Yuncong Chen, Jianwu Xu, Ding Li, Haifeng Chen","doi":"10.1145/3336191.3371847","DOIUrl":"https://doi.org/10.1145/3336191.3371847","url":null,"abstract":"Question routing (QR) aims at recommending newly posted questions to the potential answerers who are most likely to answer the questions. The existing approaches that learn users' expertise from their past question-answering activities usually suffer from challenges in two aspects: 1) multi-faceted expertise and 2) temporal dynamics in the answering behavior. This paper proposes a novel temporal context-aware model in multiple granularities of temporal dynamics that concurrently address the above challenges. Specifically, the temporal context-aware attention characterizes the answerer's multi-faceted expertise in terms of the questions' semantic and temporal information simultaneously. Moreover, the design of the multi-shift and multi-resolution module enables our model to handle temporal impact on different time granularities. Extensive experiments on six datasets from different domains demonstrate that the proposed model significantly outperforms competitive baseline models.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879738","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}
Ruirui Li, Jyun-Yu Jiang, Jiahao Liu, Chu-Cheng Hsieh, Wei Wang
Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of training instances. As a consequence, the lack of training data prevents ASR systems from accurately learning users acoustic biometrics, jeopardizes the downstream applications, and eventually impairs user experience. In this work, we propose an adversarial few-shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based few-shot learning to learn speaker acoustic representations, where the limited instances are comprehensively utilized to improve the identification performance. In addition, adversarial learning is applied to further enhance the generalization and robustness for speaker identification with adversarial examples. Experiments conducted on a publicly available large-scale dataset demonstrate that model significantly outperforms eleven baseline methods. An in-depth analysis further indicates both effectiveness and robustness of the proposed method.
{"title":"Automatic Speaker Recognition with Limited Data","authors":"Ruirui Li, Jyun-Yu Jiang, Jiahao Liu, Chu-Cheng Hsieh, Wei Wang","doi":"10.1145/3336191.3371802","DOIUrl":"https://doi.org/10.1145/3336191.3371802","url":null,"abstract":"Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of training instances. As a consequence, the lack of training data prevents ASR systems from accurately learning users acoustic biometrics, jeopardizes the downstream applications, and eventually impairs user experience. In this work, we propose an adversarial few-shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based few-shot learning to learn speaker acoustic representations, where the limited instances are comprehensively utilized to improve the identification performance. In addition, adversarial learning is applied to further enhance the generalization and robustness for speaker identification with adversarial examples. Experiments conducted on a publicly available large-scale dataset demonstrate that model significantly outperforms eleven baseline methods. An in-depth analysis further indicates both effectiveness and robustness of the proposed method.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123469923","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}
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