The Experts below are selected from a list of 4041 Experts worldwide ranked by ideXlab platform
Chengxiang Zhai - One of the best experts on this subject based on the ideXlab platform.
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context sensitive information retrieval using Implicit Feedback
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005Co-Authors: Xuehua Shen, Bin Tan, Chengxiang ZhaiAbstract:A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit Implicit Feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using Implicit Feedback, especially the clicked document summaries, can improve retrieval performance substantially.
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SIGIR - Context-sensitive information retrieval using Implicit Feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005Co-Authors: Xuehua Shen, Bin Tan, Chengxiang ZhaiAbstract:A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit Implicit Feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using Implicit Feedback, especially the clicked document summaries, can improve retrieval performance substantially.
James Caverlee - One of the best experts on this subject based on the ideXlab platform.
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Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
arXiv: Information Retrieval, 2019Co-Authors: Haochen Chen, Ziwei Zhu, James CaverleeAbstract:We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-Implicit Feedback for enriching the original sparse Implicit Feedback dataset. Three of the unique characteristics of PsiRec are: (i) it views user-item interactions as a bipartite graph and models pseudo-Implicit Feedback from this perspective; (ii) its random walks-based approach extracts graph structure information from this bipartite graph, toward estimating pseudo-Implicit Feedback; and (iii) it adopts a Skip-gram inspired measure of confidence in pseudo-Implicit Feedback that captures the pointwise mutual information between users and items. This pseudo-Implicit Feedback is ultimately incorporated into a new latent factor model to estimate user preference in cases of extreme sparsity. PsiRec results in improvements of 21.5% and 22.7% in terms of Precision@10 and Recall@10 over state-of-the-art Collaborative Denoising Auto-Encoders. Our implementation is available at this https URL.
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ICDM - Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
2018 IEEE International Conference on Data Mining (ICDM), 2018Co-Authors: Haochen Chen, Ziwei Zhu, James CaverleeAbstract:We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-Implicit Feedback for enriching the original sparse Implicit Feedback dataset. Three of the unique characteristics of PsiRec are: (i) it views user-item interactions as a bipartite graph and models pseudo-Implicit Feedback from this perspective; (ii) its random walks-based approach extracts graph structure information from this bipartite graph, toward estimating pseudo-Implicit Feedback; and (iii) it adopts a Skip-gram inspired measure of confidence in pseudo-Implicit Feedback that captures the pointwise mutual information between users and items. This pseudo-Implicit Feedback is ultimately incorporated into a new latent factor model to estimate user preference in cases of extreme sparsity. PsiRec results in improvements of 21.5% and 22.7% in terms of Precision@10 and Recall@10 over state-of-the-art Collaborative Denoising Auto-Encoders. Our implementation is available at https://github.com/heyunh2015/PsiRecICDM2018.
Xuehua Shen - One of the best experts on this subject based on the ideXlab platform.
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context sensitive information retrieval using Implicit Feedback
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005Co-Authors: Xuehua Shen, Bin Tan, Chengxiang ZhaiAbstract:A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit Implicit Feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using Implicit Feedback, especially the clicked document summaries, can improve retrieval performance substantially.
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SIGIR - Context-sensitive information retrieval using Implicit Feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005Co-Authors: Xuehua Shen, Bin Tan, Chengxiang ZhaiAbstract:A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit Implicit Feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using Implicit Feedback, especially the clicked document summaries, can improve retrieval performance substantially.
Hui Xiong - One of the best experts on this subject based on the ideXlab platform.
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AAAI - SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
2020Co-Authors: Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui XiongAbstract:The recent development of online recommender systems has a focus on collaborative ranking from Implicit Feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the Implicit Feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of Implicit Feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
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SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui XiongAbstract:The recent development of online recommender systems has a focus on collaborative ranking from Implicit Feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the Implicit Feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of Implicit Feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
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IJCAI - Sparse Bayesian content-aware collaborative filtering for Implicit Feedback
2016Co-Authors: Defu Lian, Nicholas Jing Yuan, Xing Xie, Hui XiongAbstract:The popularity of social media creates a large amount of user-generated content, playing an important role in addressing cold-start problems in recommendation. Although much effort has been devoted to incorporating this information into recommendation, past work mainly targets explicit Feedback. There is still no general framework tailored to Implicit Feedback, such as views, listens, or visits. To this end, we propose a sparse Bayesian content-aware collaborative filtering framework especially for Implicit Feedback, and develop a scalable optimization algorithm to jointly learn latent factors and hyperparameters. Due to the adaptive update of hyperparameters, automatic feature selection is naturally embedded in this framework. Convincing experimental results on three different Implicit Feedback datasets indicate the superiority of the proposed algorithm to state-of-the-art content-aware recommendation methods.
Chao Wang - One of the best experts on this subject based on the ideXlab platform.
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SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui XiongAbstract:The recent development of online recommender systems has a focus on collaborative ranking from Implicit Feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the Implicit Feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of Implicit Feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
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AAAI - SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
2020Co-Authors: Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui XiongAbstract:The recent development of online recommender systems has a focus on collaborative ranking from Implicit Feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the Implicit Feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of Implicit Feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
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multiple pairwise ranking with Implicit Feedback
Conference on Information and Knowledge Management, 2018Co-Authors: Yunzhou Zhang, Chao Wang, Qi Liu, Enhong ChenAbstract:As users Implicitly express their preferences to items on many real-world applications, the Implicit Feedback based collaborative filtering has attracted much attention in recent years. Pairwise methods have shown state-of-the-art solutions for dealing with the Implicit Feedback, with the assumption that users prefer the observed items to the unobserved items. However, for each user, the huge unobserved items are not equal to represent her preference. In this paper, we propose a Multiple Pairwise Ranking (MPR) approach, which relaxes the simple pairwise preference assumption in previous works by further tapping the connections among items with multiple pairwise ranking criteria. Specifically, we exploit the preference difference among multiple pairs of items by dividing the unobserved items into different parts. Empirical studies show that our algorithms outperform the state-of-the-art methods on real-world datasets.
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CIKM - Multiple Pairwise Ranking with Implicit Feedback
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018Co-Authors: Yunzhou Zhang, Chao Wang, Qi Liu, Ye Yuyang, Enhong ChenAbstract:As users Implicitly express their preferences to items on many real-world applications, the Implicit Feedback based collaborative filtering has attracted much attention in recent years. Pairwise methods have shown state-of-the-art solutions for dealing with the Implicit Feedback, with the assumption that users prefer the observed items to the unobserved items. However, for each user, the huge unobserved items are not equal to represent her preference. In this paper, we propose a Multiple Pairwise Ranking (MPR) approach, which relaxes the simple pairwise preference assumption in previous works by further tapping the connections among items with multiple pairwise ranking criteria. Specifically, we exploit the preference difference among multiple pairs of items by dividing the unobserved items into different parts. Empirical studies show that our algorithms outperform the state-of-the-art methods on real-world datasets.
