Shengmin Jin, Richard Wituszynski, Max Caiello-Gingold, R. Zafarani
Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.
{"title":"WebShapes: Network Visualization with 3D Shapes","authors":"Shengmin Jin, Richard Wituszynski, Max Caiello-Gingold, R. Zafarani","doi":"10.1145/3336191.3371867","DOIUrl":"https://doi.org/10.1145/3336191.3371867","url":null,"abstract":"Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"1 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":"130668760","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}
Recommendation systems tend to suffer severely from the sparse training data. A large portion of users and items usually have a very limited number of training instances. The data sparsity issue prevents us from accurately understanding users' preferences and items' characteristics and jeopardize the recommendation performance eventually. In addition, models, trained with sparse data, lack abundant training supports and tend to be vulnerable to adversarial perturbations, which implies possibly large errors in generalization. In this work, we investigate the recommendation task in the context of prospective customer recommendation in location based social networks. To comprehensively utilize the training data, we explicitly learn to compare users' historical check-in businesses utilizing self-attention mechanisms. To enhance the robustness of a recommender system and improve its generalization performance, we perform adversarial training. Adversarial perturbations are dynamically constructed during training and models are trained to be tolerant of such nuisance perturbations. In a nutshell, we introduce a Self-Attentive prospective Customer RecommendAtion framework, SACRA, which learns to recommend by making comparisons among users' historical check-ins with adversarial training. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 existing methods using two real-world datasets. The results demonstrate that SACRA significantly outperforms all baselines.
{"title":"Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks","authors":"Ruirui Li, Xian Wu, Wei Wang","doi":"10.1145/3336191.3371841","DOIUrl":"https://doi.org/10.1145/3336191.3371841","url":null,"abstract":"Recommendation systems tend to suffer severely from the sparse training data. A large portion of users and items usually have a very limited number of training instances. The data sparsity issue prevents us from accurately understanding users' preferences and items' characteristics and jeopardize the recommendation performance eventually. In addition, models, trained with sparse data, lack abundant training supports and tend to be vulnerable to adversarial perturbations, which implies possibly large errors in generalization. In this work, we investigate the recommendation task in the context of prospective customer recommendation in location based social networks. To comprehensively utilize the training data, we explicitly learn to compare users' historical check-in businesses utilizing self-attention mechanisms. To enhance the robustness of a recommender system and improve its generalization performance, we perform adversarial training. Adversarial perturbations are dynamically constructed during training and models are trained to be tolerant of such nuisance perturbations. In a nutshell, we introduce a Self-Attentive prospective Customer RecommendAtion framework, SACRA, which learns to recommend by making comparisons among users' historical check-ins with adversarial training. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 existing methods using two real-world datasets. The results demonstrate that SACRA significantly outperforms all baselines.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"82 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":"126999163","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}
Complex systems in different disciplines are usually modeled as heterogeneous networks. Different from homogeneous networks or attributed networks, heterogeneous networks are associated with complexity in heterogeneous structure or heterogeneous content or both. The abundant information in heterogeneous networks provide opportunities yet pose challenges for researchers and practitioners to develop customized machine learning solutions for solving different problems in complex systems. We are motivated to do significant work for learning from heterogeneous networks. In this paper, we first introduce the motivation and background of this research. Later, we present our current work which include a series of proposed methods and applications. These methods will be introduced in the perspectives of personalization in web-based systems and heterogeneous network embedding. In the end, we raise several research directions as future agenda.
{"title":"Learning from Heterogeneous Networks: Methods and Applications","authors":"Chuxu Zhang","doi":"10.1145/3336191.3372182","DOIUrl":"https://doi.org/10.1145/3336191.3372182","url":null,"abstract":"Complex systems in different disciplines are usually modeled as heterogeneous networks. Different from homogeneous networks or attributed networks, heterogeneous networks are associated with complexity in heterogeneous structure or heterogeneous content or both. The abundant information in heterogeneous networks provide opportunities yet pose challenges for researchers and practitioners to develop customized machine learning solutions for solving different problems in complex systems. We are motivated to do significant work for learning from heterogeneous networks. In this paper, we first introduce the motivation and background of this research. Later, we present our current work which include a series of proposed methods and applications. These methods will be introduced in the perspectives of personalization in web-based systems and heterogeneous network embedding. In the end, we raise several research directions as future agenda.","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":"131950483","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}
This tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network (RNN), long short-term memory, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, temporal difference VAE, stochastic neural network, stochastic temporal convolutional network, predictive state neural network, and policy neural network. Enhancing the prior/posterior representation is addressed. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies, tasks and applications are presented to tackle different issues in deep Bayesian mining, searching, learning and understanding. At last, we will point out a number of directions and outlooks for future studies. This tutorial serves the objectives to introduce novices to major topics within deep Bayesian learning, motivate and explain a topic of emerging importance for data mining and natural language understanding, and present a novel synthesis combining distinct lines of machine learning work.
{"title":"Deep Bayesian Data Mining","authors":"Jen-Tzung Chien","doi":"10.1145/3336191.3371870","DOIUrl":"https://doi.org/10.1145/3336191.3371870","url":null,"abstract":"This tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, \"deep learning\" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The \"semantic structure\" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The \"distribution function\" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network (RNN), long short-term memory, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, temporal difference VAE, stochastic neural network, stochastic temporal convolutional network, predictive state neural network, and policy neural network. Enhancing the prior/posterior representation is addressed. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies, tasks and applications are presented to tackle different issues in deep Bayesian mining, searching, learning and understanding. At last, we will point out a number of directions and outlooks for future studies. This tutorial serves the objectives to introduce novices to major topics within deep Bayesian learning, motivate and explain a topic of emerging importance for data mining and natural language understanding, and present a novel synthesis combining distinct lines of machine learning work.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"1 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":"129738102","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}
Changfeng Sun, Han Liu, Meng Liu, Z. Ren, Tian Gan, Liqiang Nie
Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.
{"title":"LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation","authors":"Changfeng Sun, Han Liu, Meng Liu, Z. Ren, Tian Gan, Liqiang Nie","doi":"10.1145/3336191.3371805","DOIUrl":"https://doi.org/10.1145/3336191.3371805","url":null,"abstract":"Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"1 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":"129970139","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}
Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite the previous success, existing multi-graph clustering methods mostly use shallow models, which are incapable to capture the highly non-linear structures and the complex cluster associations in multi-graph, thus result in sub-optimal results. Inspired by the powerful representation learning capability of neural networks, in this paper, we propose an end-to-end deep learning model to simultaneously infer cluster assignments and cluster associations in multi-graph. Specifically, we use autoencoding networks to learn node embeddings. Meanwhile, we propose a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph. We introduce two regularizers to leverage both within-graph and cross-graph dependencies. An attentive mechanism is further developed to learn cross-graph cluster associations. Through extensive experiments on a variety of datasets, we observe that our method outperforms state-of-the-art baselines by a large margin.
{"title":"Deep Multi-Graph Clustering via Attentive Cross-Graph Association","authors":"Dongsheng Luo, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu, Xiang Zhang","doi":"10.1145/3336191.3371806","DOIUrl":"https://doi.org/10.1145/3336191.3371806","url":null,"abstract":"Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite the previous success, existing multi-graph clustering methods mostly use shallow models, which are incapable to capture the highly non-linear structures and the complex cluster associations in multi-graph, thus result in sub-optimal results. Inspired by the powerful representation learning capability of neural networks, in this paper, we propose an end-to-end deep learning model to simultaneously infer cluster assignments and cluster associations in multi-graph. Specifically, we use autoencoding networks to learn node embeddings. Meanwhile, we propose a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph. We introduce two regularizers to leverage both within-graph and cross-graph dependencies. An attentive mechanism is further developed to learn cross-graph cluster associations. Through extensive experiments on a variety of datasets, we observe that our method outperforms state-of-the-art baselines by a large margin.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"26 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":"130346176","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}
Expert finding is a task designed to enable recommendation of the right person who can provide high-quality answers to a requester's question. Most previous works try to involve a content-based recommendation, which only superficially comprehends the relevance between a requester's question and the expertise of candidate experts by exploring the content or topic similarity between the requester's question and the candidate experts' historical answers. However, if a candidate expert has never answered a question similar to the requester's question, then existing methods have difficulty making a correct recommendation. Therefore, exploring the implicit relevance between a requester's question and a candidate expert's historical records by perception and reasoning should be taken into consideration. In this study, we propose a novel textslrecurrent memory reasoning network (RMRN) to perform this task. This method focuses on different parts of a question, and accordingly retrieves information from the histories of the candidate expert.Since only a small percentage of historical records are relevant to any requester's question, we introduce a Gumbel-Softmax-based mechanism to select relevant historical records from candidate experts' answering histories. To evaluate the proposed method, we constructed two large-scale datasets drawn from Stack Overflow and Yahoo! Answer. Experimental results on the constructed datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.
{"title":"Recurrent Memory Reasoning Network for Expert Finding in Community Question Answering","authors":"Jinlan Fu, Yi Li, Qi Zhang, Qinzhuo Wu, Renfeng Ma, Xuanjing Huang, Yu-Gang Jiang","doi":"10.1145/3336191.3371817","DOIUrl":"https://doi.org/10.1145/3336191.3371817","url":null,"abstract":"Expert finding is a task designed to enable recommendation of the right person who can provide high-quality answers to a requester's question. Most previous works try to involve a content-based recommendation, which only superficially comprehends the relevance between a requester's question and the expertise of candidate experts by exploring the content or topic similarity between the requester's question and the candidate experts' historical answers. However, if a candidate expert has never answered a question similar to the requester's question, then existing methods have difficulty making a correct recommendation. Therefore, exploring the implicit relevance between a requester's question and a candidate expert's historical records by perception and reasoning should be taken into consideration. In this study, we propose a novel textslrecurrent memory reasoning network (RMRN) to perform this task. This method focuses on different parts of a question, and accordingly retrieves information from the histories of the candidate expert.Since only a small percentage of historical records are relevant to any requester's question, we introduce a Gumbel-Softmax-based mechanism to select relevant historical records from candidate experts' answering histories. To evaluate the proposed method, we constructed two large-scale datasets drawn from Stack Overflow and Yahoo! Answer. Experimental results on the constructed datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"49 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":"114212491","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}
Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, Wei Wang
Click-through rate (CTR) prediction is a critical task in online advertising and marketing. For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks. First, they typically lack persuasive rationales to explain the outcomes of the models. Unexplainable predictions and recommendations may be difficult to validate and thus unreliable and untrustworthy. In many applications, inappropriate suggestions may even bring severe consequences. Second, existing approaches have poor efficiency in analyzing high-order feature interactions. Third, the polysemy of feature interactions in different semantic subspaces is largely ignored. In this paper, we propose InterHAt that employs a Transformer with multi-head self-attention for feature learning. On top of that, hierarchical attention layers are utilized for predicting CTR while simultaneously providing interpretable insights of the prediction results. InterHAt captures high-order feature interactions by an efficient attentional aggregation strategy with low computational complexity. Extensive experiments on four public real datasets and one synthetic dataset demonstrate the effectiveness and efficiency of InterHAt.
{"title":"Interpretable Click-Through Rate Prediction through Hierarchical Attention","authors":"Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, Wei Wang","doi":"10.1145/3336191.3371785","DOIUrl":"https://doi.org/10.1145/3336191.3371785","url":null,"abstract":"Click-through rate (CTR) prediction is a critical task in online advertising and marketing. For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks. First, they typically lack persuasive rationales to explain the outcomes of the models. Unexplainable predictions and recommendations may be difficult to validate and thus unreliable and untrustworthy. In many applications, inappropriate suggestions may even bring severe consequences. Second, existing approaches have poor efficiency in analyzing high-order feature interactions. Third, the polysemy of feature interactions in different semantic subspaces is largely ignored. In this paper, we propose InterHAt that employs a Transformer with multi-head self-attention for feature learning. On top of that, hierarchical attention layers are utilized for predicting CTR while simultaneously providing interpretable insights of the prediction results. InterHAt captures high-order feature interactions by an efficient attentional aggregation strategy with low computational complexity. Extensive experiments on four public real datasets and one synthetic dataset demonstrate the effectiveness and efficiency of InterHAt.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"42 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":"126044474","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}
Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods.
{"title":"Context-aware Deep Model for Joint Mobility and Time Prediction","authors":"Yile Chen, Cheng Long, G. Cong, Chenliang Li","doi":"10.1145/3336191.3371837","DOIUrl":"https://doi.org/10.1145/3336191.3371837","url":null,"abstract":"Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"4 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":"116815872","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}
Retrieval effectiveness in information retrieval systems is heavily dependent on how various parameters are tuned. One option to find these parameters is to run multiple online experiments and using a parameter sweep approach in order to optimize the search system. There are multiple downsides of this approach, mainly that it may lead to a poor experience for users. Another option is to do offline evaluation, which can act as a safeguard against potential quality issues. Offline evaluation requires a validation set of data that can be benchmarked against different parameter settings. However, for search over personal corpora, e.g. email and file search, it is impractical and often impossible to get a complete representative validation set, due to the inability to save raw queries and document information. In this work, we show how to do offline parameter tuning with only a partial validation set. In addition, we demonstrate how to do parameter tuning in the cases when we have complete knowledge of the internal implementation of the search system (white-box tuning), as well as the case where we have only partial knowledge (grey-box tuning). This has allowed us to do offline parameter tuning in a privacy-sensitive manner.
{"title":"Parameter Tuning in Personal Search Systems","authors":"S. Chen, Xuanhui Wang, Zhen Qin, Donald Metzler","doi":"10.1145/3336191.3371820","DOIUrl":"https://doi.org/10.1145/3336191.3371820","url":null,"abstract":"Retrieval effectiveness in information retrieval systems is heavily dependent on how various parameters are tuned. One option to find these parameters is to run multiple online experiments and using a parameter sweep approach in order to optimize the search system. There are multiple downsides of this approach, mainly that it may lead to a poor experience for users. Another option is to do offline evaluation, which can act as a safeguard against potential quality issues. Offline evaluation requires a validation set of data that can be benchmarked against different parameter settings. However, for search over personal corpora, e.g. email and file search, it is impractical and often impossible to get a complete representative validation set, due to the inability to save raw queries and document information. In this work, we show how to do offline parameter tuning with only a partial validation set. In addition, we demonstrate how to do parameter tuning in the cases when we have complete knowledge of the internal implementation of the search system (white-box tuning), as well as the case where we have only partial knowledge (grey-box tuning). This has allowed us to do offline parameter tuning in a privacy-sensitive manner.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"14 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":"128874069","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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