Streaming Ranking Based Recommender Systems

Wang, Weiqing; Yin, Hongzhi; Huang, Zi; Wang, Qinyong; Du, Xingzhong; Nguyen, Quoc Viet Hung

doi:10.1145/3209978.3210016

Cited by 90 publications

(71 citation statements)

References 29 publications

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“…Traditionally, online learning methods update the model only with the new data, which will always lead to forgetting the past [25]. To prevent the model from losing the awareness of historical data, we leverage the reservoir to maintain a long-term memory of the historical data [4,9,34,35]. The reservoir technique is widely used in the streaming database management systems.…”

Section: Wasserstein Reservoir For Streaming Modelmentioning

confidence: 99%

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Qiu

Yin

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Section: Wasserstein Reservoir For Streaming Modelmentioning

confidence: 99%

“…Recently, a few methods utilize the reservoir technique for streaming tasks [3,4,6,34,35]. In these cases, the interaction data is stored with the same probability in the reservoir and then sampled for the online training of the model.…”

Section: Introductionmentioning

confidence: 99%

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Qiu

Yin

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…User guidance. Guiding users has been studied in data integration, data repair, crowdsourcing, and recommender systems [36,67,44,70,69,68,65,31,21]. Most approaches rely on decision theoretic frameworks to rank candidate data for validation.…”

Section: Related Workmentioning

confidence: 99%

User guidance for efficient fact checking

et al. 2019

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The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation. Construction and maintenance of a knowledge base thus has to rely on fact checking, an assessment of the credibility of facts. Due to an inherent lack of ground truth information, such fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a comprehensive framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Our framework is grounded in a novel probabilistic model that combines user input with automated credibility inference. Based thereon, we show how to guide users in fact checking by identifying the facts for which validation is most beneficial. Moreover, our framework includes techniques to reduce the manual effort invested in fact checking by determining when to stop the validation and by supporting efficient batching strategies. We further show how to handle fact checking in a streaming setting. Our experiments with three real-world datasets demonstrate the efficiency and effectiveness of our framework: A knowledge base of high quality, with a precision of above 90%, is constructed with only a half of the validation effort required by baseline techniques.

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“…Participatory sensing, where participants proactively report their observations, has emerged as an important data collection paradigm. In this paradigm, human acts as the sensors or employs their own devices to perform sensing tasks [2,6,29,39,40,51,53,54,62]. Examples of these sensors include cell phone accelerometers, cameras, and GPS devices.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Efforts in Reconciling Participatory Sensing Data

Cong

Toan

Hùng

et al. 2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

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Participatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for worldwide applications, without spending cost to buy dedicated sensors. Despite of this benefit, the data collected from human sensors are inherently uncertain due to no quality guarantee from the participants. Moreover, the participatory sensing data are time series that not only exhibit highly irregular dependencies on time, but also vary from sensor to sensor. To overcome these issues, we study in this paper the problem of reconciling probabilistic data from given (uncertain) time series collected by participatory sensors. More precisely, an iterative process is executed in which we exchange between two mutual reinforcing routines: (i) aggregating probabilistic time series from multiple sensors and expert input, (ii) validating them by expert knowledge with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data.

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Streaming Ranking Based Recommender Systems

Cited by 90 publications

References 29 publications

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User guidance for efficient fact checking

Minimizing Efforts in Reconciling Participatory Sensing Data

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