DCCR is a deep collaborative conjunctive recommender model for rating prediction tasks. It is a hybrid neural network architecture consisting of an embedding system and neural network. The embedding system extracts latent features of users and items from raw ratings data. The neural network then merges the user and item features and extracts higher-level interaction features for rating prediction. Experiments on two datasets show DCCR achieves better accuracy than other methods by leveraging deep feature extraction and fusion while only using raw rating data. Future work includes exploring other similarity measures to address issues with sparse rating data.
The document discusses a content-based recommendation system with sentiment analysis. It provides an overview of recommendation systems and their importance. The objectives are to provide personalized recommendations to users based on their preferences using information filtering techniques. Existing systems faced issues like scalability, sparsity, and cold starts. The proposed system is a hybrid approach that combines item-based collaborative filtering with user clustering to make predictions. It will be scalable while addressing cold starts. Tools like Flask, JavaScript, Python are used. Cosine similarity and sentiment analysis techniques are also discussed. The conclusion is that the proposed system can recommend less popular items and future work could include other factors in recommendations.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
Collaborative filtering is a technique used in recommender systems to predict a user's preferences based on other similar users' preferences. It involves collecting ratings data from users, calculating similarities between users or items, and making recommendations. Common approaches include user-user collaborative filtering, item-item collaborative filtering, and probabilistic matrix factorization. Recommender systems are evaluated both offline using metrics like MAE and RMSE, and through online user testing.
This document discusses deep learning recommender systems from prototypes to production. It provides an overview of modern recommender systems and how deep learning techniques like neural item embeddings, similarity search, and experimentation can improve recommender systems. The key points are: (1) Deep learning allows extracting features from different data sources and generating accurate user/item representations; (2) Neural item embeddings like word2vec learn vector representations of items to find similar items; (3) Similarity search techniques like ANN enable efficient nearest neighbor search in large embedding spaces; (4) Experimentation through offline/online testing and A/B testing is important for evaluating models and improving recommendations.
The document discusses decision trees and the ID3 algorithm. It provides an overview of data mining techniques, including decision trees. It then describes the ID3 algorithm in detail, including how it uses information gain to build decision trees top-down and recursively to classify data. An example of applying the ID3 algorithm to a sample dataset is also provided to illustrate the step-by-step process.
Evaluating and Enhancing Efficiency of Recommendation System using Big Data A...IRJET Journal
This document discusses evaluating and enhancing the efficiency of recommendation systems using big data analytics. It begins with an abstract that outlines recommendation systems, collaborative filtering, and the need for big data analytics due to large datasets. It then discusses specific collaborative filtering techniques like user-based, item-based, and matrix factorization. It describes challenges like scalability that big data analytics can help address. The document evaluates recommendation algorithms using metrics like MAE, RMSE, precision and time taken on movie recommendation datasets. It aims to design an efficient recommendation system using the best techniques.
The document discusses a content-based recommendation system with sentiment analysis. It provides an overview of recommendation systems and their importance. The objectives are to provide personalized recommendations to users based on their preferences using information filtering techniques. Existing systems faced issues like scalability, sparsity, and cold starts. The proposed system is a hybrid approach that combines item-based collaborative filtering with user clustering to make predictions. It will be scalable while addressing cold starts. Tools like Flask, JavaScript, Python are used. Cosine similarity and sentiment analysis techniques are also discussed. The conclusion is that the proposed system can recommend less popular items and future work could include other factors in recommendations.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
Collaborative filtering is a technique used in recommender systems to predict a user's preferences based on other similar users' preferences. It involves collecting ratings data from users, calculating similarities between users or items, and making recommendations. Common approaches include user-user collaborative filtering, item-item collaborative filtering, and probabilistic matrix factorization. Recommender systems are evaluated both offline using metrics like MAE and RMSE, and through online user testing.
This document discusses deep learning recommender systems from prototypes to production. It provides an overview of modern recommender systems and how deep learning techniques like neural item embeddings, similarity search, and experimentation can improve recommender systems. The key points are: (1) Deep learning allows extracting features from different data sources and generating accurate user/item representations; (2) Neural item embeddings like word2vec learn vector representations of items to find similar items; (3) Similarity search techniques like ANN enable efficient nearest neighbor search in large embedding spaces; (4) Experimentation through offline/online testing and A/B testing is important for evaluating models and improving recommendations.
The document discusses decision trees and the ID3 algorithm. It provides an overview of data mining techniques, including decision trees. It then describes the ID3 algorithm in detail, including how it uses information gain to build decision trees top-down and recursively to classify data. An example of applying the ID3 algorithm to a sample dataset is also provided to illustrate the step-by-step process.
Evaluating and Enhancing Efficiency of Recommendation System using Big Data A...IRJET Journal
This document discusses evaluating and enhancing the efficiency of recommendation systems using big data analytics. It begins with an abstract that outlines recommendation systems, collaborative filtering, and the need for big data analytics due to large datasets. It then discusses specific collaborative filtering techniques like user-based, item-based, and matrix factorization. It describes challenges like scalability that big data analytics can help address. The document evaluates recommendation algorithms using metrics like MAE, RMSE, precision and time taken on movie recommendation datasets. It aims to design an efficient recommendation system using the best techniques.
Screening of Mental Health in Adolescents using ML.pptxNitishChoudhary23
This document discusses using machine learning algorithms for screening mental health in adolescents. It begins with introducing machine learning and the different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning. It then focuses on classification algorithms, describing logistic regression and how classification algorithms can be used for applications like email spam detection and cancer identification. The document also discusses software requirements like Anaconda and Python libraries like Scikit-learn, NumPy, Pandas and Matplotlib. It concludes that comparing machine learning techniques is important to identify the best for a given domain like predicting mental health.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
Recommendation engines : Matching items to usersjobinwilson
Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout and distributed computing frameworks. He also covers strategies for taking recommendation systems into production.
Recommendation engines matching items to usersFlytxt
Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout. He also covers strategies for taking recommendation systems into production.
Recommender systems aim to recommend items like books, movies, or products to users based on their preferences. There are two main approaches: collaborative filtering, which recommends items liked by similar users, and content-based filtering, which recommends items similar to those a user has liked based on item attributes. Both have strengths and weaknesses, so hybrid systems combining the approaches can provide the best recommendations.
This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
This document provides information about Mohan C R, including his education and qualifications, skills, projects, publications and awards. He has over 2 years of experience as a data scientist and machine learning engineer. He has a B.Tech in electronics and communication engineering as well as nanodegree certificates in data science and machine learning foundations from Udacity. His skills include Python, SQL, AWS, TensorFlow and he has worked on projects involving image classification, recommendation engines, disaster response pipelines and customer segmentation.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
C19013010 the tutorial to build shared ai services session 1Bill Liu
This document provides an agenda and overview for a tutorial on building shared AI services. The tutorial consists of two modules: the first module discusses a case study of AI as a service and challenges of traditional machine learning, and how deep learning can help address these challenges. The second module introduces Keras and options for running Keras on Spark, including a use case, code lab, and prerequisites for running the code lab in Docker containers.
IOTA 2016 Social Recomender System Presentation.ASHISH JAGTAP
In today’s age of ever increasing use of internet, there are around 74% active internet users out of which 60% users contribute to social networking and most of them are students from the age group 16-30. If this young generation is targeted specifically towards educational activities keeping the same social networking environment in the background would create interest in students for educational activities and also yield productive results. This can be implemented by creating a social-cum-educational portal with recommender systems. Specific information to specific student can be provided. Use of such technology can reduce the gap between students and the information which can lead to their inherent development and success! However, most of the existing Social Recommender systems do not have good scalabilities which are unable to process huge volumes of data. Aiming to this problem we can design a social recommender system based on Hadoop and its parallel computing platform.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Screening of Mental Health in Adolescents using ML.pptxNitishChoudhary23
This document discusses using machine learning algorithms for screening mental health in adolescents. It begins with introducing machine learning and the different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning. It then focuses on classification algorithms, describing logistic regression and how classification algorithms can be used for applications like email spam detection and cancer identification. The document also discusses software requirements like Anaconda and Python libraries like Scikit-learn, NumPy, Pandas and Matplotlib. It concludes that comparing machine learning techniques is important to identify the best for a given domain like predicting mental health.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
Recommendation engines : Matching items to usersjobinwilson
Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout and distributed computing frameworks. He also covers strategies for taking recommendation systems into production.
Recommendation engines matching items to usersFlytxt
Jobin Wilson is an architect at Flytxt, where he works on big data analytics and automation projects. He has previous experience with virtualization, cloud management, data center automation, and workflow systems. In this presentation, he discusses recommendation engines, how they work using collaborative filtering techniques, and the challenges of implementing them at scale using Apache Mahout. He also covers strategies for taking recommendation systems into production.
Recommender systems aim to recommend items like books, movies, or products to users based on their preferences. There are two main approaches: collaborative filtering, which recommends items liked by similar users, and content-based filtering, which recommends items similar to those a user has liked based on item attributes. Both have strengths and weaknesses, so hybrid systems combining the approaches can provide the best recommendations.
This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
This document provides information about Mohan C R, including his education and qualifications, skills, projects, publications and awards. He has over 2 years of experience as a data scientist and machine learning engineer. He has a B.Tech in electronics and communication engineering as well as nanodegree certificates in data science and machine learning foundations from Udacity. His skills include Python, SQL, AWS, TensorFlow and he has worked on projects involving image classification, recommendation engines, disaster response pipelines and customer segmentation.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
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In today’s age of ever increasing use of internet, there are around 74% active internet users out of which 60% users contribute to social networking and most of them are students from the age group 16-30. If this young generation is targeted specifically towards educational activities keeping the same social networking environment in the background would create interest in students for educational activities and also yield productive results. This can be implemented by creating a social-cum-educational portal with recommender systems. Specific information to specific student can be provided. Use of such technology can reduce the gap between students and the information which can lead to their inherent development and success! However, most of the existing Social Recommender systems do not have good scalabilities which are unable to process huge volumes of data. Aiming to this problem we can design a social recommender system based on Hadoop and its parallel computing platform.
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1. DCCR: Deep Collaborative Conjunctive Recommender
for Rating Prediction
Guide
Mrs.T.Aruna Sri
BY(CSEMPBNUM_N11)
SEELAM ROHITH REDDY 2215316447
NANNAMURI SASI KUMAR 2215316432
VAIBHAVI MUTYA 2215316456
REVANTH REDDY K 2215316442
2. ABSTRACT
Collaborative filtering combined with various kinds of deep learning models is appealing to recommender systems,
which have shown a strong positive effect in an accuracy improvement. However, Deep learning model rely heavily
on abundant information to improve prediction accuracy, which has precise data requirements in addition to raw
rating data. Furthermore, most of them ignore the interaction effect between users and items when building the
recommendation model. To address this issues, we propose DCCR, a deep collaborative conjunctive recommender,
for rating prediction tasks that are solely based on the raw ratings. A DCCR is a hybrid architecture that consists of
two different kinds of neural network models (i.e., label encoder, embedded systems and neural networks).We
present a novel recommender model that extracts deep inner features of both users and items that solely depend
on the explicit ratings and extract the interaction features. We describe the details of the structure, input vector,
loss function and training techniques, which are indispensable for the experiments.
We investigate the impacts of the parameters of the proposed model and analyze the relations of these
parameters on the prediction accuracy. An improved activation function Relu for our neural networks are proposed
, which can be specified with input vectors and TensorFlow framework. By conducting considerable experiments on
two datasets, the results show that the proposed model can achieve better accuracy for this particular rating
prediction task. We also discuss the expandability of our model by analysing the depth of neural networks. Several
methods are proposed to adjust the gradient problem of the deep neural networks.
3. Introduction
Recommender System:
Recommender systems are utilized in a variety of areas, and are most commonly recognized as playlist generators for
video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or
content recommenders for social media platforms such as Facebook and Twitter.
A recommender system or a recommendation system (sometimes replacing 'system' with a synonym such as platform or
engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to
an item. They are primarily used in commercial applications.
Business Perspective:
4. Methodologies used behind making Recommendations:
Existing System:
Majorly 2 types of filtering methodology is used
1. Content-based recommendation systems: they recommend based on product attributes. Content-based recommendation
systems are recommendation systems that use their knowledge of each product to recommend new products. Let’s say that
you tell a friend that you just watched the movie Iron Man starring Robert Downey, Jr. and that you really liked it. Your friend
might recommend that you watch the movie Avengers next. Both movies are Science fiction film and both movies feature the
same movie star. It could be a good recommendation because the movies have a lot of attributes in common. This is the idea
behind content-based recommendation systems. They try to recommend products that have similar attributes to a product
that the user already liked.
2. Collaborative filtering: they recommend based on similar users Collaborative filtering systems make recommendations only
based on how users rated products in the past, not based on anything about the products themselves. In collaborative filtering,
the recommendation system has no knowledge of the actual product it is recommending. It only knows how other users rated
the product. Collaborative filtering has a very big advantage over content-based recommendations. The advantage is that you
don’t even need to know anything about the products that you’re recommending. As long as you have user review data, you
can build a collaborative filtering recommendation system.
5. • It only works when you already have user reviews to work from. If you don’t have any reviews, you can’t make
recommendations. That means it’s difficult to recommend products to brand new users because new users haven’t
reviewed any products yet. And finally, collaborative filtering tends to favor products with lots of reviews over products
with few or very less reviews. This can make it hard for users to discover new releases since they aren’t likely to get
recommended as often.
• most of them ignore the interaction effect between users and items when building the recommendationmodel.
Limitations
6. • To address these issues, we propose DCCR, a deep collaborative conjunctive recommender, for rating prediction tasks that
are solely based on the raw ratings.
• A DCCR is a hybrid architecture that consists of two different kinds of neural network models.
• To take advantage of the deep learning model in terms of deeper inner feature extraction and fusion, the DCCR is a hybrid
architecture that consists of two different kinds of neural network models (i.e., ES and NN). ES extracts user and item
deeper latent features for the raw ratings data in a separate way, while the main function of an NN is to merge the user
and item feature from the results of the ES at the first layer and extract the higher features (i.e., relationships between user
and item) based on the combined user and item features. Rating prediction of the DCCR model. The input vector is the
feature representation of users and items. The output vector is the predicted ratings..
Proposed System
9. TECHNICAL REQUIREMENTS
• DATASET-for Training Our Model
• Machine Learning Algorithms-for building our model
• Python 3.7 Packages:
• NumPy: for the data handling and operations.
• Pandas: for the data Structure
• H20: for Machine Learning Algorithms, rapidly turning over models
• Matplotlib: for Data Visualization
• Seaborn: for data visualization
HARDWARE REQUIREMENTS
To Run Python a minimum of 4GB Ram is required. Anything Below 4GB would result
in huge differences in results and also Slows down our work. A Minimum Integrated GPU
should be available in the systems that will help understand the visualizations better.
Software Requirements
10. Anaconda-Jupyter Notebooks Directly from the platform and without involving DevOps,data
scientists can develop and deploy AI and machine learning models rapidly into production.
Anaconda provides the tools needed to easily:
IMPLEMENTATION TOOL
11. Tensor flow
Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the
brain. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep
learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too
special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations,
while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated
between these edges. TensorFlow can hardware, and software requirements can be classified into
FRAMEWORK
12. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It
was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible
delay is key todoing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping
(through user friendliness, modularity, and extensibility).Supports both convolutional networks and recurrent networks, as
well as combinations of the two. Runs seamlessly on CPU and GPU.
Keras
13. Neural Network
Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that
constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed
with task-specific rules.
14. Pandas: Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its
powerful data structures. The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional
data. In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for
analysis of data.
NumPy: NumPy is a Python package. It stands for 'Numerical Python'. It is a library consisting of multidimensional array
objects and a collection of routines for processing of array. Numeric, the ancestor of NumPy, was developed by Jim Hugunin.
Another package Numarray was also developed, having some additional functionalities. In 2005, Travis Oliphant created
NumPy package by incorporating the features of Numarray into Numeric package. There are many contributors to this open
source project.
Seaborn: Seaborn is a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated
with pandas data structures.
Matplotlib:Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension
NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like
Tkinter, wxPython, Qt, or GTK+.
Packages
16. Loading Dataset
We can load the data directly by dragging and dropping the dataset into jupyter notebook homepage. We are using pandas to
load the data. We will also use pandas next to explore the data both with descriptive statistics and data visualization.
34. CONCLUSION
Collaborative filtering has shown to be effective in commercial recommender systems. By combining with neural networks,
CF can represent the latent features of users and items without a manual setting. However, most of related studies use a
single model to perform a rating prediction task without considering the traits of features and ratings. In this model ,we
propose a hybrid neural network model for rating prediction that is named the deep collaborative conjunctive
recommender (DCCR). This model integrates the neural network and label encoder to separately capture the latent
features from users and items and describes the interactions between these features. Numerous factors affect the
prediction performance. Thus, to achieve the optimal model, we evaluate the DCCR with varying factor settings by
considerable contrast experiments. The results show that our DCCR model outperforms other state-of-the-art methods
using two real-world datasets.
35. FUTURE SCOPE
Cosine similarity calculation do not work well when we don't have enough rating for movie or when user's rating for some
movie is exceptionally either high or low.As an improvement on this project some other methods such as adjusted cosine
similarity can be used to compute similarity.
Adjusted cosine similarity, which is similar to cosine similarity, is measured by normalizing the user vectors Ux and Uy and
computing the cosine of the angle between them. However, unlike cosine similarity, when computing the dot product of
the two user vectors, adjusted cosine similarity uses the deviation between each of the user’s item ratings, denoted Ru,
and their average item rating, denoted ¯Ru, in place of the user’s raw item rating. The main advantage of this approach is
that in item-based collaborative filtering, the item vectors consist of ratings from different users who often have varying
rating scales.
36. References
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recommender systems,'' Found. Trends Hum.-Comput. Interact., vol. 4,
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• [2] R. Kumar, B. K. Verma, and S. S. Rastogi, ``Social popularity basedSVDCC recommender system,'' Int. J.
Comput. Appl., vol. 87, no. 14,
pp. 3337, 2014.
• [3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, ``Imagenet classication
with deep convolutional neural networks,'' in Proc. Adv. Neural Inf. Pro-
cess. Syst., 2012, pp. 10971105.
• [4] Y. Jhamb, T. Ebesu, and Y. Fang, ``Attentive contextual denoising autoen-
coder for recommendation,'' in Proc. ACM SIGIR Int. Conf. Theory Inf.
Retr. New York, NY, USA: ACM, 2018, pp. 2734.
• [5] X. Cai, J. Han, and L. Yang, ``Generative adversarial network based het-
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recommendation,'' in Proc. 32nd AAAI Conf. Artif. Intell., 2018, pp. 18.
• [6] Y. Peng, S.Wang, and B.-L. Lu, ``Marginalized denoising autoencoder via
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Process. Berlin, Germany: Springer, 2013, pp. 156163.
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