Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
These slides gives you information about path testing and data flow testing in software testing. These slides will be helpful for Engineering, CSE students.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
These slides gives you information about path testing and data flow testing in software testing. These slides will be helpful for Engineering, CSE students.
Web Engineering- Web Application Categories & Characteristics Book Gerti KappelARVIND PANDE
Web Engineering Categories & Characteristics of Web Application PPTs based on Book- Web Engineering; Author- Gerti Kappel, Birgit , Siegfried Reich, Werner
Web Tutorial - https://digitalpadm.com/categories-of-web-applications/
Message and Stream Oriented CommunicationDilum Bandara
Message and Stream Oriented Communication in distributed systems. Persistent vs. Transient Communication. Event queues, Pub/sub networks, MPI, Stream-based communication, Multicast communication
Distributed Operating System,Network OS and Middle-ware.??Abdul Aslam
Define Distributed Operating System, Network Operating System and Middle-ware? Differentiate between DOS, NOS and Middle-ware? Define the goals of each? ???
Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Imag...SOYEON KIM
K. So Yeon, B. Yenewondim, and S. Kyung-Ah, "Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Image Detection Using Scale Invariant Feature Transform Image Descriptor.", Information Science and Applications, LNEE 339, 2015, in press.
Web Engineering- Web Application Categories & Characteristics Book Gerti KappelARVIND PANDE
Web Engineering Categories & Characteristics of Web Application PPTs based on Book- Web Engineering; Author- Gerti Kappel, Birgit , Siegfried Reich, Werner
Web Tutorial - https://digitalpadm.com/categories-of-web-applications/
Message and Stream Oriented CommunicationDilum Bandara
Message and Stream Oriented Communication in distributed systems. Persistent vs. Transient Communication. Event queues, Pub/sub networks, MPI, Stream-based communication, Multicast communication
Distributed Operating System,Network OS and Middle-ware.??Abdul Aslam
Define Distributed Operating System, Network Operating System and Middle-ware? Differentiate between DOS, NOS and Middle-ware? Define the goals of each? ???
Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Imag...SOYEON KIM
K. So Yeon, B. Yenewondim, and S. Kyung-Ah, "Investigating the Effectiveness of E-mail Spam Image Data for Phone Spam Image Detection Using Scale Invariant Feature Transform Image Descriptor.", Information Science and Applications, LNEE 339, 2015, in press.
Survey on Credit Card Fraud Detection Using Different Data Mining Techniquesijsrd.com
In today's world of e-commerce, credit card payment is the most popular and most important mean of payment due to fast technology. As the usage of credit card has increased the number of fraud transaction is also increasing. Credit card fraud is very serious and growing problem throughout the world. This paper represents the survey of various fraud detection techniques through which fraud can be detected. Although there are serious fraud detection technology exits based on data mining, knowledge discovery but they are not capable to detect the fraud at a time when fraudulent transaction are in progress so two techniques Neural Network and Hidden Markov Model(HMM) are capable to detect the fraudulent transaction is in progress. HMM categorizes card holder profile as low, medium, and high spending on their spending behavior. A set of probability is assigned to each cardholder for amount of transaction. The amount of incoming transaction is matched with cardholder previous transaction, if it is justified a predefined threshold value then a transaction is considered as a legitimate else it is considered as a fraud.
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
Level Up Your Python Code Reviews to Ship It Faster - WWCodeDigital - 2020Syeda Kauser Inamdar
LinkedIn - https://www.linkedin.com/in/syedakauserinamdar/ | Twitter - @codealatte
Top takeaways
* How improving your code review cycle helps to 'ship it' or get approved faster
* How to integrate python tooling to automate parts of your review
* How to perform a review (both submitting and giving feedback) with a learning mindset
Conference Link - https://connectdigital.womenwhocode.dev/day-1/
https://www.slideshare.net/SyedaKauserInamdar/level-up-your-python-code-reviews-to-ship-it-faster-wwcodedigital-2020
Fraud Detection in Online Reviews using Machine Learning Techniquesijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Are you a software developer who wants to improve your skills? Are you a team lead who enjoys mentoring other developers and improving both their and your coding skills? Or are you a manager who needs to improve the knowledge sharing among your software developers across different projects? Today we will be talking about how Code Reviews can do just this and also improve the code quality of your software projects in the process. Code Review can be a great way to get to know your fellow developers and even learn a different or sometimes better way to build a software solution. At the end of the session you will have a better understanding of the benefits of Code Review as well as some great tips to use in your team today.
A look how to manage, grow and ignite customer content in the form of online reviews, testimonials and case studies. It's content your customers trust and seek out. Prevention given as the closing keynote at the Social Media Rockstar Event 2016.
Control Your Online Reputation - MSP Social Media BreakfastAaron Weiche
My presentation covering online review, customer testimonials and case studies. Learn the power of online reputation, customer feedback, Net Promoter Score, rich snippets and more.
Evaluating Brand Attributes in a Digital EnvironmentFlexMR
Understanding the value of your brand is a key indicator of your position in the marketplace. But an exact evaluation of brands, including both tangible financial data as well as customer perceptions and intangible brand equity is difficult to achieve. One small mistake in understanding customer perception can lead businesses astray and have devastating consequences.
Fortunately, it is easier than ever before to evaluate brand attributes and build a clear picture of customer opinion. In this session, we aim to show you the diverse range of creative tools available to help align your brand and customer perceptions - as well as building this in to your financial models, giving an accurate overview of brand assets and intangible financial equity.
Book a free demo of our market research platform to see the full demonstration: http://bit.ly/1MK0jG3
You want to improve your software skills. That’s a given. You may be a mentor or a manager who needs to improve the knowledge sharing among your software developers across different projects. Code Reviews can do just that while improving code quality in your projects. Code Review not only builds developer team spirit but also offers new ways to improve a software solution. You’ll walk away from this session with in-depth understanding of Code Review to strengthen your team.
The Journey from User Acquisition to Advocacy - CleverTap & AppFollowCleverTap
In this webinar, AppFollow and CleverTap explore how brands should optimize experience right from being discovered, converting visitors into customers, retaining these customers and turning them into brand advocates.
1. Click here to retrieve the Risk Management Template. Working wi.docxjackiewalcutt
1. Click here to retrieve the Risk Management Template. Working with the RFP you have selected and this template create a risk matrix and management plan for your project.
The Risk Management Template is filled out so you can see the level of detail needed. This template must be tailored to your specific project and presented as if you are giving this to your client, which means uniform font, color, graphics and a professional look.
The document should include a title page, verbiage on why risk management is important to your selected project and how it relates to the project (two to three paragraphs), the modified risk table, and a conclusion.
· Each risk must be scored, and mitigation plans must be outlined in detail.
· Each risk must be ranked, and therefore, the highest risk items should be on top: red, yellow, green in order.
Listing at least 10-15 risks is appropriate.
Unit VI Mini Project
Value Creation
Sellers create value for buyers in the form of providing leading edge technology, state of the art products, timely services, ease of buying, and quality of relationships, services, and products. These values are usually related to company finances (but not for all criteria, as compliance, safety, and other factors can be considered). Sellers will create value for you as the buyer, given the RFP you have selected, and then in turn you will create value for your buyer (client).
Taking into considerations the RFP you have selected to work with during this course, answer the following two questions in detail. Submit this assignment as one comprehensive MS Word document of no less than 200 words.
1. What are the most important value add criteria your sellers can provide you in order to make your project a success?
2. In turn, what value will you provide to your clients? Why should your clients select you as their future vendor?
Unit VII Mini Project
CMROAT
Use the RFP you have selected to work with during this course. Given the following list of possible risks/opportunities, name four that come into play, and give details about how each of the four you selected can impact the RFP (risks and opportunities) you have selected if it were to be a real project. How can these risks be mitigated? How can the opportunities (if any) be used to improve the project and procurement relationship? Submit this assignment as one comprehensive MS Word document of no less than 500 words.
· Budget
· Schedule
· Seller Experience
· Quality
· Technology
· Geography (location of buyer, seller, associates)
· Financial
· Strategic Direction (for both buyer and seller)
At the end of the course, a final procurement plan is due (the Procurement Plan Template is accessible from Syllabus Unit VIII Course Project below). Please take the time to begin working on this during Unit VII so you have enough time to complete it by the end of the official course date. This is a detailed document relating to the RFP you have selected.
BBA ...
UserZoom Education Series - Research Deep Dive - Advanced - Task-Based TOL (b...UserZoom
Today you will learn about benchmarking studies. Specifically, you will learn how to conduct a competitor benchmarking study on live websites.
After this session you will be able to:
Know when to use benchmarking
Know the type of data you can collect with benchmarking
Create a benchmarking study in UserZoom
Interpret the results of a benchmark
Robust Pathway-based Multi-Omics Data Integration using Directed Random Walk ...SOYEON KIM
17th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA 2018)
Cancer Data Integration Challenge (http://camda.info/)
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated wi...SOYEON KIM
Summary of paper "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts",
Silver M, Chen P, Li R, Cheng C-Y, Wong T-Y, et al.
In PLOS Genetics, 2013
A survey of heterogeneous information network analysisSOYEON KIM
A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Member, IEEE,
Yitong Li, Jiawei Zhang, Yizhou Sun, Member, IEEE,
and Philip S. Yu, Fellow, IEEE
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015
Translated Learning: Transfer learning across different feature spaces
Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu.
In Proceedings of Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Semi-automatic ground truth generation using unsupervised clustering and limi...SOYEON KIM
Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition
Szilárd Vajda, Yves Rangoni, Hubert Cecotti
Pattern Recognition Letters, 2015
Evaluating color descriptors for object and scene recognitionSOYEON KIM
Van De Sande, Koen EA, Theo Gevers, and Cees GM Snoek. "Evaluating color descriptors for object and scene recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1582-1596.
Outcome-guided mutual information networks for investigating gene-gene intera...SOYEON KIM
TBC2014 poster
"Outcome-guided mutual information networks for investigating gene-gene interaction effects on clinical outcomes", Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Opinion Spam and Analysis
Nitin Jindal and Bing Liu
(WSDM, 2008)
1
2. three types of spam reviews
• Type 1 (untruthful opinions): also known as fake reviews or bogus
reviews.
2
deliberately mislead
readers or opinion mining systems
1.To promote some target objects
(hype spam).
2. To damage the reputation of
some other target objects
(defaming spam).
3. three types of spam reviews
• Type 2 (reviews on brands only): not comment on the products for
the products but only the brands, the manufacturers or the sellers.
• Type 3 (non-reviews): two main sub-types:
• advertisements
• other irrelevant reviews containing no opinions (questions, answers ..)
• They are not targeted at the specific products and are often biased.
3
4. Review Data from
amazon.com
• Each amazon.com’s review consists of 8 parts
• <Product ID> <Reviewer ID> <Rating> <Date> <Review Title> <Revie
>
4
1
2
3 45
6
7 8
6. Reviews, Reviewer, and
Products
6
Figure 1. Log-log plot of number of reviews
to number of members for amazon.
Figure 2. Log-log plot of number of reviews
to number of products for amazon.
• There are 2 reviewers with more than 15,000 reviews.
• 68% of reviewers wrote only 1 review.
• 50% of products have only 1 review.
• Only 19% of the products have at least 5 reviews.
7. Review Ratings and
Feedbacks
7
Figure 3. Log-log plot of number of reviews
to number of feedbacks for amazon.
Figure 4. Rating vs. percent of reviews
• 60% of the reviews have a rating of 5.0.
• On average, a review gets 7 feedbacks.
• The percentage of positive feedbacks of a review decreases rapidly
from the first review of a product to the last.
8. Spam Detection
• a classification problem with two classes, spam and non-spam.
• manually label training examples for spam reviews of type 2
and type 3.
• recognizing whether a review is an untruthful opinion spam
(type 1) is extremely difficult by manually reading the review.
• found a large number of duplicate and near-duplicate reviews.
• 1. Duplicates from different userids on the same product.
• 2. Duplicates from the same userid on different products.
• 3. Duplicates from different userids on different products.
8
9. Detection of Duplicate Reviews
• shingle method (2-grams)
• Jaccard distance (Similarity score) > 90% duplicates.
• The maximum similarity score : the maximum of similarity
scores between different reviews of a reviewer.
9
10. Detecting Type 2 & Type 3
• Model Building Using Logistic Regression
• Manually labeled 40 spam reviews of the two types.
• It produces a probability estimate of each review being a spam.
10
11. Feature Identification and
Construction
• There are three main types of information
• (1) the content of the review,
• (2) the reviewer who wrote the review,
• (3) the product being reviewed.
• three types of features:
• (1) review centric features,
• (2) reviewer centric features,
• (3) product centric features.
• three types based on their average ratings
• Good (rating ≥ 4), bad (rating ≤ 2.5) and Average, otherwise
11
12. Review Centric Features
• number of
feedbacks(F1)
• number of helpful
feedbacks(F2)
• percent of helpful
feedbacks(F3)
• length of the review
title(F4)
• length of review
body(F5)
• Position of the
review of a product
sorted by date.
ascending (F6) and
descending (F7)
• the first review (F8)
• the only review (F9)
12
13. Review Centric Features -Textual
features• percent of positive bearing
words(F10)
• percent of negative bearing
words(F11)
• cosine similarity (F12)
• percent of times brand
name (F13)
• percent of numerals
words(F14)
• percent of capitals
words(F15)
• percent of all capital
words(F16)
13
• rating of the review (F17)
• the deviation from product rating (F18)
• the review is good, average or bad (F19)
• a bad review was written just after the first good review of
the product and vice versa (F20, F21)
Review Centric Features –Rating related features
14. Reviewer Centric Features
• Ratio of the number of reviews that the reviewer wrote which
were the first reviews (F22)
• ratio of the number of cases in which he/she was the only
reviewer (F23)
• average rating given by reviewer (F24)
• standard deviation in rating (F25)
• the reviewer always gave only good, average or bad rating
(F26)
• a reviewer gave both good and bad ratings (F27)
• a reviewer gave good rating and average rating (F28)
• a reviewer gave bad rating and average rating (F29)
• a reviewer gave all three ratings (F30)
• percent of times that a reviewer wrote a review with binary
features F20 (F31) and F21 (F32).
14
15. Product Centric Features
• Price of the product (F33)
• Sales rank of the product (F34)
• Average rating (F35)
• standard deviation in ratings (F36)
15
16. Results of Type 2 and Type 3 Spam
Detection
• logistic regression on the data using 470 spam reviews for
positive class and rest of the reviews for negative class.
• 10-fold cross validation.
16
17. Analysis of Type 1 Spam Reviews
• 1. To promote some target objects (hype spam).
• 2. To damage the reputation of some other target
objects (defaming spam).
17
18. Making Use of Duplicates
• the same person writes the same review for different versions
of the same product may not be spam.
• We propose to treat all duplicate spam reviews as positive
examples, and the rest of the reviews as negative examples.
• We then use them to learn a model to identify non-duplicate
reviews with similar characteristics, which are likely to be spam
reviews.
18
19. Model Building Using
Duplicates
• building the logistic regression model using duplicates and
non-duplicates is not for detecting duplicate spam
• Our real purpose is to use the model to identify type 1 spam
reviews that are not duplicated.
• check whether it can predict outlier reviews
19
20. • Outlier reviews : whose ratings deviate from the average
product rating a great deal.
• Sentiment classification techniques may be used to
automatically assign a rating to a review solely based on its
review content.
20
21. Predicting Outlier Reviews
• Negative deviation is considered as less than -1 from the
mean rating and positive deviation as larger than +1 from the
mean rating of the product.
• Spammers may not want their review ratings to deviate too
much from the norm to make the reviews too suspicious.
21
23. Some Other Interesting Reviews
- Only Reviews
• We did not use any position features of reviews (F6, F7, F8, F9, F20 and
F21) and number of reviews of product (F12, F18, F35, and F36) related
features in model building to prevent overfitting.
• only reviews are very likely to be candidates of spam
23
24. - Reviews from Top-Ranked Reviewers
• Top-ranked reviewers generally write a large number of reviews, much
more than bottom ranked reviewers.
• Top ranked reviewers also score high on some important indicators of
spam reviews.
top ranked reviewers are less trustworthy as compared to bottom ranked
reviewers.
24
25. - Reviews with Different Levels of Feedbacks
• If usefulness of a review is defined based on the feedbacks
that the review gets, it means that people can be readily
fooled by a spam review.
feedback spam
25
26. - Reviews of Products with Varied Sales
Ranks
• Spam activities are more limited to low selling products.
difficult to damage reputation of a high selling or popular
product by writing a spam review.
26
27. Conclusions & Future work
• Results showed that the logistic regression model is highly
effective.
• It is very hard to manually label training examples for type 1
spam.
• We will further improve the detection methods, and also look
into spam in other kinds of media, e.g., forums and blogs.
27
Editor's Notes
We consider them as spam because they are not targeted at the specific products and are often biased.
&lt;number&gt;
We can use the probability to weight each review. Since the probability reflects the likelihood that a review is a spam, those reviews with high probabilities can be weighted down to reduce their effects on opinion mining.
1.3.5 write by owners or manufacturers.
2.4.6 write by competititors
1.4 are not damaging, 2.3.5.6 are very harmful.
spam detection techniques should focus on identifying reviews in these regions.
Since the number of such duplicate spam reviews is quite large, it is reasonable to assume that they are a fairly good and random sample of many kinds of spam.
To further confirm its predictability, we want to show that it is also predictive of reviews that are more likely to be spam and are not duplicated, i.e., outlier reviews.
They are more likely to be spam reviews than average reviews because high rating deviation is a necessary condition for a harmful spam review but not sufficient because some reviewers may truly have different views from others.