Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Thanks to Maya Hristakeva for creating some of the slides.
A recommendation system, often referred to as a recommender system or recommendation engine, is a type of machine learning application that provides personalized suggestions or recommendations to users. These systems are widely used in various domains to help users discover products, services, or content that are likely to be of interest to them. There are several approaches to building recommendation systems in machine learning:
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
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المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
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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.
Machine learning has become an important tool in the modern software toolbox, and high-performing organizations are increasingly coming to rely on data science and machine learning as a core part of their business. eBay introduced machine learning to its commerce search ranking and drove double-digit increases in revenue. Stitch Fix built a multibillion dollar clothing retail business in the US by combining the best of machines with the best of humans. And WeWork is bringing machine-learned approaches to the physical office environment all around the world. In all cases, algorithmic techniques started simple and slowly became more sophisticated over time. This talk will use these examples to derive an agile approach to machine learning, and will explore that approach across several different dimensions. We will set the stage by outlining the kinds of problems that are most amenable to machine-learned approaches as well as describing some important prerequisites, including investments in data quality, a robust data pipeline, and experimental discipline. Next, we will choose the right (algorithmic) tool for the right job, and suggest how to incrementally evolve the algorithmic approaches we bring to bear. Most fancy cutting-edge recommender systems in the real world, for example, started out with simple rules-based techniques or basic regression. Finally, we will integrate machine learning into the broader product development process, and see how it can help us to accelerate business results
Modern Perspectives on Recommender Systems and their Applications in MendeleyMaya Hristakeva
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Joint work with Kris Jack, Chief Data Scientist at Mendeley.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
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).
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.
5. Problem Statement
• Google Example
– 10 billion web pages
– Average size of webpage= 20KB
– 10 billion*20KB = 200TB
– Disk read bandwidth = 50 MB/sec
– Time to read = 4 million seconds = 46+
days
– Even longer to do something useful
with the data
6. • The world is an over-crowded place
Problem Statement
7. • They all want to get our attention
Problem Statement
8. Problem Statement
• Mobile recommendations
To design such algorithm so that
overcome the current challenges and
problems of existing recommendation
system and get better accuracy to the
consumers as well as marketers
9. Search Engine vs Recommender System
“The Web is leaving the era of search and
entering one of discovery. What's the difference?
Search is what you do when you're looking for
something.
Discovery is when something wonderful that you
didn't know existed, or didn't know how to ask
for, finds you.” – CNN Money, “The race to
create a 'smart' Google
14. Top 10 High SAR rate phones
• Alcatel 1010 - 1.08
• BlackBerry Bold 9790 - 1.73
• BlackBerry Curve 9320 - 1.56
• HTC Desire X - 1.59
• Motorola V50 - 1.19
• Nokia 105 - 1.48
• Nokia 8810 - 1.14
• Nokia Asha 503 - 1.44
• Samsung S300 - 1.14
15. • We will consider standard value of SAR
is 0.9 to 1.4
• If its value is below and above to this
range means its means its dangerous
for health.
SAR(specific absorption rates)
16. Take The Survey
Few references are as follows:
• A Web-based personalized recommendation system for mobile phone
selection: Design, implementation, and evaluation
• Mobile Recommender Systems Francesco Ricci Faculty of Computer Science
Free University of Bolzano, Italy
• United State Patent Linden et al.
17. The Purpose of this study
• prioritize the design features
• prioritize the design aspects of cell phones
22. Regression Analysis for Proportions
When the response variable is a proportion or a binary
value (0 or 1), standard regression techniques must be
modified. STATGRAPHICS provides two important
procedures for this situation: Logistic Regression and Probit
Analysis. Both methods yield a prediction equation that is
constrained to lie between 0 and 1.
NOTE: We used R-language to find regression values for
data sets of different features.
23. Regression analysis
• It is used to model the relationship
between a response variable and one or
more predictor variables.
STATGRAPHICS provides a large number
of procedures for fitting different types
of regression models
30. Ranking Approaches:
– Collaborative filtering: “Tell me what
is popular amongst my peers”
– Content Based: “Show me more of
what I liked”
– Knowledge Based: “Tell me what fits
my needs”
– Hybrid
32. • Consider user x
• Find set N of other user whose ratings are
similar to x’s rating
• Estimate x’s rating based on rating of user in N
Collaborative filtering
33. • Pros:
– Extremely powerful and efficient
– Very relevant recommendations
– (1) The bigger the database,
– (2) the more the past behaviors, the
better the recommendations
Collaborative filtering
34. • Cons:
– Difficult to implement, resource and time-
consuming
– What about a new item that has never been
purchased?
Cannot be recommended
– What about a new customer who has never
bought anything? Cannot be compared to
other customers
no items can be recommended
Collaborative filtering
35. Item Profile :-
• Description of items
• Profile is a set of feature or set of
important words
• Convenient to think of important item
profile as a vector
Content Based Algorithm
36. • How to pick important words?????
• Usually from text mining
Content Based Algorithm
37. Boolean utility matrix
• Items are movies, only feature is
“actor”
• Suppose user x has watched 5 movies
• 2 movies featuring actor A
• 3 movies featuring actor B
• User profile=mean of item profile
38. PROBLEMS
• Cold Start Problem
• 1) occurs when new user or item enter
in the system
• Synonymy
• 1) when an item is represented with
two or more different names or
• entries having similar meanings
39. • Problem of providing recommendations when
there is not yet data available
• Item cold-start : A new item has been
added to the database (e.g., when a new
movie or book is released) but has not yet
received enough ratings to be
recommendable.
• User cold-start : A new user has joined the
system but their preferences are not yet
known
PROBLEMS
40. • Shilling Attacks
• when malicious user or competitor
enters into a system and starts giving
false ratings on some items Privacy
• Feeding personal information to the
recommender systems results in better
recommendation services but may lead
to issues of data privacy and security
PROBLEMS
41. • Limited Content Analysis and
Overspecialization
• The limited availability of content leads
to problems including overspecialization
• Grey Sheep
• occurs in pure CF systems where
opinions of a user do not match with
any group
PROBLEMS
44. Why Genetic Algorithms ?
• Exact methods or mathematical models
require lot of computational effort to solve
multi objective optimization problems.
• For real-life complex problems, not only
exact methods but also simple heuristic
techniques fail to obtain optimal/near-
optimal solutions efficiently.
45. Why Genetic Algorithms ?
Multiobjective evolutionary algorithms such
as genetic algorithms, nondominated sorting
genetic algorithm-II are suitable for searching
a true Results.
Widely-used in business, science, medical and
engineering
Optimization and Search Problems
Scheduling and Timetabling
46. Introduction to GA
• Genetic Algorithms are good at taking large,
potentially huge search spaces and navigating
them, looking for optimal combinations of
things, solutions you might not otherwise
find in a lifetime.”- Salvatore Mangano,
Computer Design, May 1995.
• The genetic algorithm (GA) is
a search heuristic that mimics the process of
natural evolution
47. GA is inspired from Nature
Natural Selection
Darwin's theory of evolution:-
only the organisms best adapted to their
environment tend to survive and transmit
their genetic characteristics in increasing
numbers to succeeding generations while those
less adapted tend to be eliminated.
48. Basic genetic algorithms
• Step 1: Represent the problem variable domain as a
chromosome of a fixed length, choose the size of a
chromosome population N, the crossover probability pc and
the mutation probability pm.
• Step 2: Define a fitness function to measure the
performance, or fitness, of an individual chromosome in the
problem domain. The fitness function establishes the basis
for selecting chromosomes that will be mated during
reproduction.
• Step 3: Randomly generate an initial population of
chromosomes of size N:
x1, x2 , . . . , xN
• Step 4: Calculate the fitness of each individual chromosome:
f (x1), f (x2), . . . , f (xN)
50. Flow Diagram For Mobile Recommender System
» Mobile Recommendation
Mobiles
Extract
Mobile
features
Database Extracting Records Analyzing GA
Display
Recommendations
51. Population Initialization
There are two primary methods to initialize a population
in a GA.
They are −
• Random Initialization − Populate the initial population
with completely random solutions.
• Heuristic initialization − Populate the initial population
using a known heuristic for the problem
52. • It has been observed that the entire population should
not be initialized using a heuristic, as it can result in the
population having similar solutions and very little diversity.
It has been experimentally observed that the random
solutions are the ones to drive the population to
optimality. Therefore, with heuristic initialization, we just
seed the population with a couple of good solutions, filling
up the rest with random solutions rather than filling the
entire population with heuristic based solutions.
• It has also been observed that heuristic initialization in
some cases, only effects the initial fitness of the
population, but in the end, it is the diversity of the
solutions which lead to optimality.
Population Initialization
53. Genetic Algorithms:
Recommender System
• The fitness function simply defined is a
function which takes a candidate solution
to the problem as input and produces as
output how “fit” our how “good” the
solution is with respect to the problem
in consideration.
55. • Suppose that the size of the chromosome population N
is 6, the crossover probability pc equals 0.7, and the
mutation probability pm equals 0.01. The fitness
function in our example is defined by
F(x, y, z, w)= 0.7 X + 0.6 Y + 0.55 Z + 0.5 W
59. Roulette wheel selection
• The most commonly used chromosome selection techniques is the
roulette wheel selection.
60. Crossover operator
• In our example, we have an initial population of 6
chromosomes. Thus, to establish the same population in
the next generation, the roulette wheel would be spin six
times.
• Once a pair of parent chromosomes is selected, the
crossover operator is applied.
• First, the crossover operator randomly chooses a crossover
point where two parent chromosomes “break”, and then
exchanges the chromosome parts after that point. As a
result, two new offspring are created.
• If a pair of chromosomes does not cross over, then the
chromosome cloning takes place, and the offspring are
created as exact copies of each parent.
62. Mutation operator
• Mutation represents a change in the gene.
• Mutation is a background operator. Its role is to provide a
guarantee that the search algorithm is not trapped on a local
optimum.
• The mutation operator flips a randomly selected gene in a
chromosome.
• The mutation probability is quite small in nature, and is kept
low for GAs, typically in the range between 0.001 and 0.01.
• We have taken one point crossover.
65. After 50 iteration we found
these results
RAM Processor Camera SM
8 2.3 16 64
8 2.7 16 128
6 2.1 20 64
8 2.2 15 64
7 2.2 18 128
8 2.1 19 128
Genetic Analysis shows that this recommendation
system is optimized both for consumers and
marketers.
81. FUTURE ENHANCEMENT
In future we will work on Genetic Algorithm on different
cross-over like 1-crossover,2-crossover, α-crossover, β-
crossover so that we will predict product for consumer as
well as marketers.
In future we will create a form so that we know the
current position of user that this particular user would
purchase an product or not. But user is closer to purchase
we recommend different EMI and various ways to save
money and as well as Increase the Company’s revenue.
82. We will Implement different regressions
techniques on the dataset so that we will
predict the user preference.
For combining previous 3-steps our ultimate
goal is to Implement the hybrid algorithm
for efficient and accurate results under
complex user environment.
FUTURE ENHANCEMENT