USER'S INSTRUCTIONS: The project work you are about to view is on "a recommendation system using data mining techniques". Please, sit back and study the below research material carefully. This project topic "a recommendation system using data mining techniques" have complete 5(five) Chapters. The complete Project Material/writeup include: Abstract + Introduction + etc + Literature Review + methodology + etc + Conclusion + Recommendation + References/Bibliography.Our aim of providing this "a recommendation system using data mining techniques" project research material is to reduce the stress of moving from one school library to another all in the name of searching for "a recommendation system using data mining techniques" research materials. We are not encouraging any form of plagiarism. This service is legal because, all institutions permit their students to read previous projects, books, articles or papers while developing their own works.

---

TITLE PAGE

 

BY

---
--/H2013/01430
DEPARTMENT OF ----
SCHOOL OF ---
INSTITUTE OF ---

DECEMBER,2018

---

APPROVAL PAGE

This is to certify that the research work, "a recommendation system using data mining techniques" by ---, Reg. No. --/H2007/01430 submitted in partial fulfillment of the requirement award of a Higher National Diploma on --- has been approved.

By
---                                                     . ---
Supervisor                                                  Head of Department.
Signature……………….                           Signature……………….        

……………………………….
---
External Invigilator

---

DEDICATION
This project is dedicated to Almighty God for his protection, kindness, strength over my life throughout the period and also to my --- for his financial support and moral care towards me.Also to my mentor --- for her academic advice she often gives to me. May Almighty God shield them from the peril of this world and bless their entire endeavour Amen.

---

ACKNOWLEDGEMENT

The successful completion of this project work could not have been a reality without the encouragement of my --- and other people. My immensely appreciation goes to my humble and able supervisor mr. --- for his kindness in supervising this project.
My warmest gratitude goes to my parents for their moral, spiritual and financial support throughout my study in this institution.
My appreciation goes to some of my lecturers among whom are Mr. ---, and Dr. ---. I also recognize the support of some of the staff of --- among whom are: The General Manager, Deputy General manager, the internal Auditor Mr. --- and the ---. Finally, my appreciation goes to my elder sister ---, my lovely friends mercy ---, ---, --- and many others who were quite helpful.

---

PROJECT DESCRIPTION: This work "a recommendation system using data mining techniques" research material is a complete and well researched project material strictly for academic purposes, which has been approved by different Lecturers from different higher institutions. We made Preliminary pages, Abstract and Chapter one of "a recommendation system using data mining techniques" visible for everyone, then the complete material on "a recommendation system using data mining techniques" is to be ordered for. Happy viewing!!!

---

CHAPTER ONE

1.0            I N T R O D U C T I O N

In a world where the number of choices can be overwhelming, recommender systems help users find  and  evaluate items of interest.   They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and  knowledge management.   For  example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations  for  additional  products.
Many different algorithmic approaches have been  applied to the  basic  problem of making accurate and efficient recommender systems. The earliest “recommender systems” were content filtering systems designed to fight information overload in textual domains. These were often based on traditional information-filtering and information-retrieval systems. Recommender systems that incorporate information- retrieval methods are frequently used to satisfy ephemeral needs (short-lived, often one-time needs) from relatively static databases. For example, requesting a recommendation for a book preparing a sibling for a new child in the family. Conversely, recommender  systems  that  incorporate information-filtering methods are frequently used to satisfy persistent information (long-lived, often frequent, and specific) needs from relatively stable databases in domains with a rapid turnover or frequent additions. For example,  recommending  AP stories  to a user  concerning the latest news regarding a senator’s re-election campaign.
Without computers, a person often receives recommendations by listening to what people around him have to say. If many people in the office state that they enjoyed a particular movie, or if someone he tends to agree with suggests a given book, then he may  treat  these  as recommendations.
These products can  be recommended based on the  top  overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the  consumer as a prediction for future buying behavior. This study will  address the technology used  to generate recommendations, focusing on the  application of data mining techniques.

1.2                                      STATEMENT OF THE PROBLEM
In this competitive world every product has its reviews and the ratings given by the users on the e-commerce sites they are using. The new users are always willing to go through the product reviews before buying that particular product (Nehete et al., 2014). Same is in the case of movies people will read the reviews of the movie they want to watch. Meanwhile, the increasing online information leads to the information overload problem. Different techniques has been used in the past but they have data-sparsity and poor performance accuracy challenge. To deal with this problem recommender system automatically suggests the item to the particular user according to the user’s profile or the ratings (Yang et al., 2014) and also, using data mining techniques overcome the data-sparsity drawback and improve the performance accuracy.

1.3                                  OBJECTIVES OF THE STUDY
This work aimed at making a comprehensive study of recommendation system using data mining techniques
The objectives of the study are:

1. To prioritize and personalize the data.
2. To determine the interest of the users and to help the users in making the search easier
3. To generate an influential algorithm for data mining techniques.

1.4                             SCOPE AND LIMITATION OF THE STUDY
The scope of this work covers making a compressive study on recommendation system using data mining techniques. Recommendation systems are useful tools for the users as they provide the actual suggestions according to their likes and dislikes.
It can perform its task only when the user’s past information, his or her browsing history, previous purchases and the feedback is available.

1.5 Terms and Definitions
Association Rules: Used to associate items in a database sharing some relationship (e.g. co- purchase information). Often takes the for “if this, then that” such as “If the customer buys a handheld videogame then the customer is likely to purchase batteries.”

Collaborative Filtering: Selecting content based on the preferences of people with similar interests.
Meta-recommenders: Provide users with personalized control over the generation of a single recommendation list formed from the combination of rich recommendation data from multiple information sources and recommendation techniques.
Nearest-Neighbor Algorithm: A recommendation algorithm that calculates the distance  between  users based on the degree of correlations between scores in the users’ preference histories.
Predictions of how much a user will like an item are computed by taking the weighted average of the opinions of a set of nearest neighbors for that item.
Recommender Systems: Any system that provides a recommendation, prediction, opinion, or user-configured list of items that assists the user in evaluating items.
Social Data-Mining: Analysis and redistribution of information from records of social activity such as newsgroup postings, hyperlinks, or system usage history.
Temporal Recommenders: Recommenders that incorporate time into the recommendation process. Time can be either an input to the recommendation function, or the output of the function.

 

---

CHAPTER TWO: The chapter one of this work has been displayed above. The complete chapter two of "a recommendation system using data mining techniques" is also available. Order full work to download. Chapter two of "a recommendation system using data mining techniques" consists of the literature review. In this chapter all the related work on "a recommendation system using data mining techniques" was reviewed.

CHAPTER THREE: The complete chapter three of "a recommendation system using data mining techniques" is available. Order full work to download. Chapter three of "a recommendation system using data mining techniques" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.

CHAPTER FOUR: The complete chapter four of "a recommendation system using data mining techniques" is available. Order full work to download. Chapter four of "a recommendation system using data mining techniques" consists of all the test conducted during the work and the result gotten after the whole work

CHAPTER FIVE: The complete chapter five of "a recommendation system using data mining techniques" is available. Order full work to download. Chapter five of "a recommendation system using data mining techniques" consist of conclusion, recommendation and references.

 

---

To "DOWNLOAD" the complete material on this particular topic above click "HERE"

Do you want our Bank Accounts? please click HERE

To view other related topics click HERE

To "SUMMIT" new topic(s), develop a new topic OR you did not see your topic on our site but want to confirm the availiability of your topic click HERE

Do you want us to research your new topic? if yes, click "HERE"

Do you have any question concerning our post/services? click HERE for answers to your questions

---

For more information contact us through any of the following means:

Mobile No :+2348146561114 or +2347015391124 [Mr. Innocent]

Email address :engr4project@gmail.com

Watsapp No :+2348146561114

---

COUNTRIES THAT FOUND OUR SERVICES USEFUL

Australia, Botswana, Canada, Europe, Ghana, Ireland, India, Kenya, Liberia, Malaysia, Namibia, New Zealand, Nigeria, Pakistan, Philippines, Singapore, Sierra Leone, South Africa, Uganda, United States, United Kindom, Zambia, Zimbabwe, etc
Support: +234 8146561114 or +2347015391124

Watsapp No :+2348146561114

Email Address :engr4project@gmail.com

---

FOLLOW / VISIT US VIA: