14,242 research outputs found
The Mediation Effect of Trusting Beliefs on the Relationship Between Expectation-Confirmation and Satisfaction with the Usage of Online Product Recommendation
Online Product Recommendations (OPRs) are increasingly available to onlinecustomers as a value-added self-service in evaluating and choosing a product.Research has highlighted several advantages that customers can gain from usingOPRs. However, the realization of these advantages depends on whether and towhat extent customers embrace and fully utilise them. The relatively low OPR USAgerate indicates that customers have not yet developed trust in OPRs’ performance.Past studies also have established that satisfaction is a valid measure of systemperformance and a consistent significant determinant of users’ continuous systemusage. Therefore, this study aimed to examine the mediation effect of trustingbeliefs on the relationship between expectation-confirmation and satisfaction. Theproposed research model is tested using data collected via an online survey from626 existing users of OPRs. The empirical results revealed that social-psychologicalbeliefs (perceived confirmation and trust) are significant contributors to customersatisfaction with OPRs. Additionally, trusting beliefs partially mediate the impactof perceived confirmation on customer satisfaction. Moreover, this study validatesthe extensions of the interpersonal trust construct to trust in OPRs and examinesthe nomological validity of trust in terms of competence, benevolence, andintegrity. The findings provide a number of theoretical and practical implications. 
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
Critical success factors for preventing E-banking fraud
E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems
A study on text-score disagreement in online reviews
In this paper, we focus on online reviews and employ artificial intelligence
tools, taken from the cognitive computing field, to help understanding the
relationships between the textual part of the review and the assigned numerical
score. We move from the intuitions that 1) a set of textual reviews expressing
different sentiments may feature the same score (and vice-versa); and 2)
detecting and analyzing the mismatches between the review content and the
actual score may benefit both service providers and consumers, by highlighting
specific factors of satisfaction (and dissatisfaction) in texts.
To prove the intuitions, we adopt sentiment analysis techniques and we
concentrate on hotel reviews, to find polarity mismatches therein. In
particular, we first train a text classifier with a set of annotated hotel
reviews, taken from the Booking website. Then, we analyze a large dataset, with
around 160k hotel reviews collected from Tripadvisor, with the aim of detecting
a polarity mismatch, indicating if the textual content of the review is in
line, or not, with the associated score.
Using well established artificial intelligence techniques and analyzing in
depth the reviews featuring a mismatch between the text polarity and the score,
we find that -on a scale of five stars- those reviews ranked with middle scores
include a mixture of positive and negative aspects.
The approach proposed here, beside acting as a polarity detector, provides an
effective selection of reviews -on an initial very large dataset- that may
allow both consumers and providers to focus directly on the review subset
featuring a text/score disagreement, which conveniently convey to the user a
summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be
published in the Journal of Cognitive Computation, available at Springer via
http://dx.doi.org/10.1007/s12559-017-9496-
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Web 2.0: ew Rules for Tourism Marketing
Web 2.0 technologies challenge many tourism marketers since they have to change their old marketing beliefs and quickly learn how to best capitalize on Web 2.0 technologies. Recognizing the growing importance of Web 2.0 technologies in tourism, this paper seeks to provide a new marketing framework to help travel marketers better understand the changing marketing environment and also to identify research opportunities for tourism researchers. The new marketing functions extended by Web 2.0 technologies are discussed based on current marketing literature. Further, case studies are presented to illustrate how these functions could be translated into practical tourism marketing strategies
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules
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