Fake online reviews: Literature review, synthesis, and directions for future research

2020

J Wireless Com Network

With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

Section: Collaborative Filtering Algorithmmentioning

confidence: 99%

“…MLP-LSTM: MLP-LSTM is an online purchasing behavior prediction model proposed in literature [18], where MLP predicts user-purchasing intention by inputting user information and LSTM uses click-stream data to predict the probability for users to leave the website without trading.…”

Section: Comparison Modelsmentioning

confidence: 99%

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

2020

J Wireless Com Network

“…Furthermore, CF algorithms transform the prediction problem of user purchasing behaviors into processing of rating prediction problem, and the prediction result highly depends on user rating information for commodities. A large number of the existing researches have found that falsity and arbitrariness problems exist in user rating information for commodities [18], which restricts the prediction accuracy of CF algorithms.…”

Section: Related Work 21 Collaborative Filtering Algorithmmentioning

confidence: 99%

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

2020

Preprint

With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors were extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

“…Furthermore, CF algorithms transform the prediction problem of user purchasing behaviors into processing of rating prediction problem, and the prediction result highly depends on user rating information for commodities. A large number of the existing researches have found that falsity and arbitrariness problems exist in user rating information for commodities [13] , which restricts the prediction accuracy of CF algorithms.…”

Section: Related Work 21 Collaborative Filtering Algorithmmentioning

confidence: 99%

“…N L l    (9) Slicing input data is the key to realizing multi-granularity deep forest. Assume "sliding window" size as   (10) Slice size is lL WW  , and slice number of the whole dataset is: (11) After multi-granularity scanning, the slices of all input data are input into the random forest If there are RF n random forests, the quantity of class vectors generated through "sliding window" sampling is: (13) The final output data size after multi-granularity scanning is: (14) Fig . 9 shows the overall process of cascaded deep forest with three "sliding windows", the sizes of which are d/16, d/8 and d/4, respectively, where d represents feature number.…”

Section: Fig 6 Structural Illustration Of Cascaded Deep Forestmentioning

confidence: 99%

Application of Sample Balance-Based Multi-Perspective Feature Ensemble Learning for Prediction of User Purchasing Behaviors on Mobile Wireless Network Platforms

2020

Preprint

In recent years, with the rapid development of wireless communication network, M-Commerce has achieved great success. Relying on mobile phones, tablets and other wireless communication devices for online shopping has become a mainstream way for users to consume. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, specifically including: 1) “Sliding window”-centroid under-sampling was combined with sample balance method was used, while the positive sample size was enlarged using “sliding window”, centroid under-sampling was used to reduce the negative sample size within “sliding window”, so as to acquire user’s historical purchasing behavioral data with sample balance. 2) Influence feature of user purchasing behaviors were extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. 3) An ensemble learning model—five-fold cross validation stacking—which could be used to predict user purchasing behaviors was raised. Three prediction models—XGBoost-Logistics, LightGBM-L2 and cascaded deep forest models—so that they could realize mutual collaboration and the overall prediction ability of the ensemble learning model could be improved. 4) Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.