Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Gong, Jianya; Gao, Xiaoxia; Cheng, Hong; Liu, Jihui; Song, Yanqing; Zhang, Mantang; Zhao, Yi

doi:10.1007/s11432-017-9243-7

Cited by 13 publications

(11 citation statements)

References 26 publications

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“…Alexandridis et al [28] utilized one probability distribution to model the user-user and user-item relations. The work in [29] integrated a weighted average method (WAM) into the random walk (RW) framework to employ social ties, behavior context, and personal information.…”

Section: Random Walk Algorithmmentioning

confidence: 99%

A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

Li¹,

Zhuang²,

et al. 2019

Sci. China Inf. Sci.

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In online shopping, most of consumers will not clear their return reasons when submitting return requests (e.g., select the option "other reasons"). Prior literature mostly investigates into the return event at the transaction level, and the underlying force of returns remains untracked. To deal with this problem, we propose a machine learning algorithm named as trust-aware random walk model (TARW). In the proposed model, four patterns of consumers can be identified in terms of return forces: (i) selfish consumers, (ii) honest consumers, (iii) fraud consumers, and (iv) irrelevant consumers. To profile consumers' return patterns, we capture consumers' similarities in order preferences and return tendencies separately. Based on consumers' similarities, we obtain a return pattern trust network by introducing the trust network and collaborative filtering algorithms. Subsequently, we develop two important applications based on the trust network: (i) estimating consumers' return propensities for product types; (ii) scoring the anomaly for consumers' returns for one product. Finally, we conduct extensive experiments with the real-world data to validate the model's effectiveness in predicting and tracing consumers' returns. With the proposed model, we can help retailers improve the conversion rates of selfish consumers, retain honest consumers, and block fraud consumers. et al. A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping. Sci China Inf Sci, 2019, 62(5): 052101, https://doi.

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Section: Random Walk Algorithmmentioning

confidence: 99%

A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

Li¹,

Zhuang²,

et al. 2019

Sci. China Inf. Sci.

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show abstract

“…B. [20] et al designed Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method into the random walk framework by seamlessly employing social ties, behavior context, and personal information. References [21], [22] construct neural network models to mine deep information between features respectively from emotional analysis and semantics of social relationships.…”

Section: Related Workmentioning

confidence: 99%

EWA-GBDT: A Novel Air Quality Prediction Model Combining Exponentially Weighted Averages and Gradient Boosting Decision Tree

Gong¹,

Wang²,

Chen³

et al. 2019

IJMO

Self Cite

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Air quality prediction is a hot topic in the field of meteorology. Challenges still exist following consideration of the uncertainty of atmospheric pollutant emission sources, as well as the multi-dimensional, multi-scale, and non-stationary characteristics of meteorological environment data. For example, traditional statistical forecasting methods usually fit a nonlinear relationship between meteorological features and pollutants, which is why it is extremely difficult to learn their models. To address these challenges, we propose a novel air quality prediction model combining Exponentially Weighted Averages and Gradient Boosting Decision Tree (EWA-GBDT). More specifically, we first collected two real-world datasets, including 1) the daily concentration data of six pollutants from 01/01/2014 to 31/12/2016 and 2) the daily concentration data of meteorological features in the cities over Shijiazhuang and Xingtai from 01/01/2014 to 02/28/2017. Then, we extracted 13 types of meteorological features using the Support Vector Machine Recursive Feature Elimination method. From the respective of pollutant concentration, these features are the highest correlated with each other. Next, we applied the EWA principle to compute the above features and pollutant concentration for obtaining meteorological expectation values. Finally, considering the excellent overall prediction performance of ensemble learning, we utilized its GBDT algorithm to predict the concentration value of pollutants and thus output the air quality level. We conducted experiments on the two datasets, and the results demonstrate that EWA-GBDT outperforms other baseline methods in terms of root-mean-square deviation, mean absolute error, and the square of R.

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“…These correlations can be achieved by learning network structures and user attribute information, and we can analyze user correlations for link prediction. Various methods such as similarity computation [19,20], random walks [21,22], topic model, and probability model [23,24] are used for prediction. This section focuses on the research on link prediction and application of the LDA topic model in recent years.…”

Section: Related Workmentioning

confidence: 99%

Correlations multiplexing for link prediction in multidimensional network spaces

Xiao

Liu

et al. 2018

Sci. China Inf. Sci.

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Uncovering the network evolution mechanism by link prediction SCIENTIA SINICA Physica, Mechanica & Astronomica 41, 816 (2011); Link prediction in social networks: the state-of-the-art SCIENCE CHINA Information Sciences 58, 011101 (2015); Exploring link generation in a temporal evolution network for human mobility SCIENCE CHINA Technological Sciences 59, 16 (2016); Network selection based on available link bandwidth in multi-access networks 数字通信和网络 2, 15 (2016); SCIENCE CHINA Information Sciences

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Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Cited by 13 publications

References 26 publications

A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping

EWA-GBDT: A Novel Air Quality Prediction Model Combining Exponentially Weighted Averages and Gradient Boosting Decision Tree

Correlations multiplexing for link prediction in multidimensional network spaces

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