New online recommendation approach based on unbalanced linguistic label with integrated cloud

Kybernetes ◽  
2018 ◽  
Vol 47 (7) ◽  
pp. 1325-1347 ◽  
Author(s):  
Meng-Xian Wang ◽  
Jian-qiang Wang
Keyword(s):  
Recommendation System ◽  
Cloud Model ◽  
Real Data ◽  
Online Reviews ◽  
Linguistic Information ◽  
Content Type ◽  
Conversion Model ◽  
Linguistic Label ◽  
User Clustering ◽  
Value Range

Purpose Online reviews increasingly present the characteristic of bidirectional communication with the advent of Web 2.0 era and tend to be asymmetrical and individualized in linguistic information. The authors aim to develop a new linguistic conversion model that exploits the asymmetric and personalized information from online reviews to express such linguistic information. A new online recommendation approach is provided. Design/methodology/approach The necessity of new linguistic conversation model is elucidated, and a leverage factor is incorporated into the linguistic label of negative review to handle the asymmetry problems of linguistic scale. A possible value range of the leverage factor is studied. A new linguistic conversation model is accordingly established with an unbalanced linguistic label and a cloud model. The authors develop a new online recommendation approach based on several modules, such as initialization, conversion, user-clustering and recommendation models. Findings The unbalanced effect between negative and positive reviews is verified with real data and measured using indirect methods. A new online recommendation approach of electronic products is proposed and used as an illustrative example to prove the practicality, effectiveness and feasibility of the proposed approach. Research limitations/implications Due to the unavailable transaction information of customers, the limitation of this study is the effectiveness of the authors’ established recommendation system for platform or website cannot be verified. Originality/value In most existing studies, the influence of negative review is counterbalanced by positive review, and the unbalanced effect between negative and positive reviews is ignored. The negative review receives much attention from consumers and businesses. This study thus highlights the influence of negative review.

Providing consumers with a representative subset from online reviews

2017 ◽  
Vol 41 (6) ◽  
pp. 877-899 ◽  
Author(s):  
Jin Zhang ◽  
Ming Ren ◽  
Xian Xiao ◽  
Jilong Zhang
Keyword(s):  
Large Scale ◽  
User Study ◽  
Real Data ◽  
Online Reviews ◽  
Heuristic Approach ◽  
Online Information ◽  
Consumer Decision Making ◽  
High Coverage ◽  
Content Type ◽  
Representative Subset

Purpose The purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information. Design/methodology/approach A heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data. Findings The proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability, and is more adaptive to big data applications. Research limitations/implications The paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews. Practical implications The proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results. Originality/value The proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information.

The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services

2020 ◽  
Vol 31 (3) ◽  
pp. 465-487 ◽  
Author(s):  
Carla Ruiz-Mafe ◽  
Enrique Bigné-Alcañiz ◽  
Rafael Currás-Pérez
Keyword(s):  
Structural Model ◽  
Online Reviews ◽  
Moderating Effect ◽  
Online Information ◽  
Bias Effect ◽  
Content Type ◽  
Digital Services ◽  
Information Cues ◽  
Cognitive Information ◽  
The Impact

PurposeThis paper analyses the interrelationships between emotions, the cognitive information cues of online reviews and intention to follow the advice obtained from digital platforms, paying special attention to the moderating effect of the sequencing of review valence.Design/methodology/approachThe data were collected from 830 Spanish Tripadvisor users. In a two-step approach, a measurement model was estimated and a structural model analysed to test the proposed hypotheses. SmartPLS 3.0 software was used. The moderating effect of sequencing of reviews is tested.FindingsThe data analysis showed a bias effect of review sequence on the impact of online information cues and emotions on intention to follow advice obtained from Tripadvisor. When the online reviews of a restaurant begin with positive commentaries, their perceived persuasiveness is a stronger driver of the pleasure and arousal elicited by online reviews than when they begin with negative reviews. On the other hand, the perceived helpfulness of online reviews only triggers arousal when the user reads negative, followed by positive, comments. The impact of pleasure on intention to follow the advice provided in an online travel community is higher with positive-negative than with negative-positive sequences.Originality/valueWhile researchers have demonstrated the benefits of customer reviews on company sales, a largely uninvestigated issue is the interplay between emotions and cognitive information cues in the processing of online reviews. This is one of the first studies to examine the moderating effect of conflicting reviews on the impact of emotions and cognitive information cues on consumer intention to follow the advice obtained from digital services.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

scholarly journals Research on comment target extracting in Chinese online shopping platform

2018 ◽  
Vol 2 (3) ◽  
pp. 247-258
Author(s):  
Zhishuo Liu ◽  
Qianhui Shen ◽  
Jingmiao Ma ◽  
Ziqi Dong
Keyword(s):  
Association Rule ◽  
Online Shopping ◽  
Low Frequency ◽  
Online Reviews ◽  
Content Type ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Frequency Feature ◽  
Practical Implications ◽  
Data Reading

Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.

scholarly journals A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 177-191
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
Urban Pollution ◽  
Real Data ◽  
Movement Behavior ◽  
Daily Movement ◽  
Passenger Transportation ◽  
Ride Sharing ◽  
Mobility Behavior ◽  
User Movement

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

Effective customer selection for marketing campaigns based on net scores

2017 ◽  
Vol 11 (1) ◽  
pp. 2-15 ◽  
Author(s):  
René Michel ◽  
Igor Schnakenburg ◽  
Tobias von Martens
Keyword(s):  
Decision Trees ◽  
Direct Marketing ◽  
Real Data ◽  
Business Case ◽  
Added Value ◽  
Scoring Method ◽  
Content Type ◽  
Effective Selection ◽  
Selection For ◽  
Response Modeling

Purpose This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods. Design/methodology/approach First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches. Findings Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators. Practical implications The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures. Originality/value Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ2-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

Thermal performance assessment of cool roofs on supermarkets through case analysis in 13 cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juan Carlos Ríos-Fernández
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Real Data ◽  
Research Approach ◽  
Environmental Implications ◽  
Content Type ◽  
Cool Roof ◽  
Increase Energy Efficiency ◽  
Cool Roofs

PurposeThis paper aims to study the use of cool roof technology to avoid unnecessary energy consumption in supermarkets. This will allow to reduce and even cancel the heat absorbed by the roofs, transferring it to the buildings and thus, creating more sustainable cities.Design/methodology/approachThirteen real supermarkets with cool roofs were analysed in Australia, Canada, the USA and Spain. An analysis of so many supermarkets located in different parts of the world with different climatic zones has allowed an inductive analysis, obtaining real data of energy consumption associated with the air conditioning installations for a year with and without implementing the cool roof technology.FindingsThe paper provides insights on how the use of cool roof managed to reduce the need for energy for heating, ventilating and air conditioning by between 3.5 and 38%. Additionally, this technology reduces the annual generation of carbon dioxide (CO2) emissions per square meter of supermarket up to 2.7 kgCO2/m2. It could be an economical technology to apply in new and old buildings with a period of average economic recovery of four years.Research limitations/implicationsBecause of the chosen research approach, the research results may be generalisable. Therefore, researchers are encouraged to test proposals in construction with other uses.Practical implicationsThe paper includes economic and environmental implications for the development of cool roof technology and smooths the way for its implementation to increase energy efficiency in commercial buildings.Originality/valueThis paper is an innovative contribution to the application of cool roof technology as a source of energy savings in commercial construction through the analysis of supermarkets located in different countries with different climate zones. This will help other researchers to advance in this field and facilitate the implementation of the technology.

The role of key online reviews in affecting online hotel booking: an empirical investigation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dong Zhang ◽  
Pengkun Wu ◽  
Chong Wu
Keyword(s):  
Binary Logistic Regression ◽  
Online Reviews ◽  
Limited Attention ◽  
Content Type ◽  
Factors Affecting ◽  
Mediating Role ◽  
Antecedents And Consequences ◽  
Hotel Managers ◽  
Hotel Booking

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziang Wang ◽  
Feng Yang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Product Evaluation ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Attributes ◽  
Data Set ◽  
Content Type ◽  
Face To Face ◽  
Online Retailers ◽  
Dirichlet Allocation

Purpose It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment. Originality/value This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

The importance of user-generated photos in restaurant selection

2019 ◽  
Vol 10 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Bruno Oliveira ◽  
Beatriz Casais
Keyword(s):  
Design Methodology ◽  
Online Reviews ◽  
User Generated Content ◽  
Content Type ◽  
Online Platforms ◽  
Photo Sharing ◽  
Restaurant Managers ◽  
Marketing Activities ◽  
Hospitality Sector ◽  
Practical Implications

Purpose User-generated content and online reviews are highly relevant in purchase decision in the hospitality sector, including restaurants, but there is a lack of knowledge about the effect of sharing pictures in this context. This study aims to focus on the relevance of user-generated photos in online platforms for restaurants’ selection. Design/methodology/approach A research was conducted with a sample of 319 residents of Porto region, who had at least one meal in a restaurant over the 30 days before the answer of the survey and had searched online to select the restaurant. Findings The results show that while doing online research about restaurants, it is important for potential consumers to find pictures of food and physical evidences of restaurants generated by other users. Findings also show that consumers find user-generated photos especially at websites of reviews, although the importance of restaurant owned platforms, such as official social media pages and websites. Practical implications The research results appeal restaurant managers to understand the importance of user-generated photos in online platforms by promoting photo sharing in their restaurants with appropriate marketing activities for that purpose. Originality/value This paper expands the state-of-the-art about the importance of user-generated content, focusing on the importance of photos from restaurants shared by consumers in online platforms.

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