On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data

2021 ◽  
Author(s):  
Young Kwark ◽  
Gene Moo Lee ◽  
Paul A. Pavlou ◽  
Liangfei Qiu
Keyword(s):  
Machine Learning ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Product Reviews ◽  
Learning Models ◽  
Complementary Products ◽  
Clickstream Data ◽  
Online Product Reviews ◽  
The Mean ◽  
Machine Learning Models

We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

scholarly journals A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

2020 ◽  
Vol 13 (7) ◽  
pp. 155
Author(s):  
Zhenlong Jiang ◽  
Ran Ji ◽  
Kuo-Chu Chang
Keyword(s):  
Machine Learning ◽  
Risk Aversion ◽  
Financial Data ◽  
Rolling Horizon ◽  
Learning Models ◽  
Market Index ◽  
Out Of Sample ◽  
The Mean ◽  
Mean Risk ◽  
Machine Learning Models

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

scholarly journals Leverage electron properties to predict phonon properties via transfer learning for semiconductors

2020 ◽  
Vol 6 (45) ◽  
pp. eabd1356
Author(s):  
Zeyu Liu ◽  
Meng Jiang ◽  
Tengfei Luo
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Materials Informatics ◽  
Learning Models ◽  
Materials By Design ◽  
Property Relation ◽  
Composition Property ◽  
The Mean ◽  
Machine Learning Models ◽  
Phonon Properties

Electron properties are usually easier to obtain than phonon properties. The ability to leverage electron properties to help predict phonon properties can thus greatly benefit materials by design for applications like thermoelectrics and electronics. Here, we demonstrate the ability of using transfer learning (TL), where knowledge learned from training machine learning models on electronic bandgaps of 1245 semiconductors is transferred to improve the models, trained using only 124 data, for predicting various phonon properties (phonon bandgap, group velocity, and heat capacity). Compared to directly trained models, TL reduces the mean absolute errors of prediction by 65, 14, and 54% respectively, for the three phonon properties. The TL models are further validated using several semiconductors outside of the 1245 database. Results also indicate that TL can leverage not-so-accurate proxy properties, as long as they encode composition-property relation, to improve models for target properties, a notable feature to materials informatics in general.

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structure-based Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
Accelerate Structure ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate structure-based virtual screening using machine learning models. It has been validated using datasets both from literature (12 datasets, each containing three million molecules docked with FRED) and in-house sources (one dataset of four million compounds docked with Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of the top one percent scoring molecules after docking 10 % of the dataset for the literature data, whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

scholarly journals INVESTIGATING INTER-RATER RELIABILITY OF QUALITATIVE TEXT ANNOTATIONS IN MACHINE LEARNING DATASETS

2020 ◽  
Vol 1 ◽  
pp. 21-30
Author(s):  
N. El Dehaibi ◽  
E. F. MacDonald
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
User Generated Content ◽  
Product Reviews ◽  
Learning Models ◽  
Reliability Measures ◽  
Rater Reliability ◽  
Machine Learning Models

AbstractAn important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.

scholarly journals Air temperature prediction using different machine learning models

2021 ◽  
Vol 22 (1) ◽  
pp. 534
Author(s):  
Rana Muhammad Adnan ◽  
Zhongmin Liang ◽  
Alban Kuriqi ◽  
Ozgur Kisi ◽  
Anurag Malik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Temperature ◽  
Support Vector ◽  
Group Method ◽  
Time Lags ◽  
Learning Models ◽  
Temperature Forecasting ◽  
The Mean ◽  
Prediction Capability ◽  
Machine Learning Models

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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