Personalized Review Recommendation based on Users’ Aspect Sentiment

2020 ◽  
Vol 20 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Chunli Huang ◽  
Wenjun Jiang ◽  
Jie Wu ◽  
Guojun Wang
Keyword(s):  
Review Recommendation

scholarly journals n-6 Fatty acids and cardiovascular health: a review of the evidence for dietary intake recommendations

2010 ◽  
Vol 104 (6) ◽  
pp. 788-796 ◽  
Author(s):  
Sébastien Czernichow ◽  
Daniel Thomas ◽  
Eric Bruckert
Keyword(s):  
Dietary Intake ◽  
Cardiovascular Health ◽  
Critical Role ◽  
Epidemiological Studies ◽  
Protective Role ◽  
Cvd Risk ◽  
Pufa Intake ◽  
Cholesterol Levels ◽  
Excessive Consumption ◽  
Review Recommendation

n-6 PUFA are well known for their critical role in many physiological functions and seem to reduce risks of CHD. However, some argue that excessive consumption of n-6 PUFA may lead to adverse effects on health and therefore recommend reducing dietary n-6 PUFA intake or fixing an upper limit. In this context, the present work aimed to review evidence on the link between n-6 PUFA and risks of CVD. Epidemiological studies show that n-6 PUFA dietary intake significantly lowers blood LDL-cholesterol levels. In addition, n-6 PUFA intake does not increase several CVD risk factors such as blood pressure, inflammatory markers, haemostatic parameters and obesity. Data from prospective cohort and interventional studies converge towards a specific protective role of dietary n-6 PUFA intake, in particular linoleic acid, against CVD. n-6 PUFA benefits are even increased when SFA intake is also reduced. In regards to studies examined in this narrative review, recommendation for n-6 PUFA intake above 5 %, and ideally about 10 %, of total energy appears justified.

Design Preference Centered Review Recommendation: A Similarity Learning Approach

2011 ◽  
Author(s):  
Jian Jin ◽  
Ying Liu ◽  
Ping Ji ◽  
Richard Fung
Keyword(s):  
Classification Problem ◽  
Online Reviews ◽  
Learning Approach ◽  
Similarity Learning ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Review Helpfulness ◽  
Review Recommendation ◽  
Design Preference ◽  
The Relationship

The rise of e-commerce websites like Amazon and Alibaba is changing the way how designers seek information to identify customer preferences in product design. From the feedbacks posted by consumers, either positive or negative, product designers can monitor the trend of consumers’ perception with respect to their product offerings and make efforts to improve accordingly. Starting from feature extraction from review documents, existing methods in identifying helpful online reviews regard the helpfulness prediction problem as a regression or classification problem and have not considered the relationship between customer reviews. Also, these approaches only consider the online helpfulness voting ratio or a unified helpfulness rating as the gold criteria for helpfulness evaluation and neglect various personal preferences from product designers. Therefore, in this paper, the focus is on how to predict reviews’ helpfulness by taking into account the personal preferences from both reviewers and designers. We start to analyze review helpfulness from both a generic aspect and a personal preference aspect. Classification methods and the proposed review similarity learning approach are utilized to estimate from the generic angle of helpfulness, while nearest neighbourhood based methods are adopted to reflect concerns from personal perspectives. Finally, a regression algorithm is called upon to predict review helpfulness based on the inputs from both aspects. Our experimental study, using a large quantity of review data crawled from Amazon and real ratings from product designers demonstrates the effectiveness of our approach and it opens a possibility for customized helpful review routing.

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