1.1 Introduction
This Online book selling websites helps to buy the books online with Recommendation system which is one of the stronger tools to increase profit and retaining buyer. The an Online Recommendation System Using Collaborative Filtering must recommend books that are of buyer’s interest. Recommendation systems are widely used to recommend products to the end users that are most appropriate. This system uses features of collaborative filtering to produce efficient and effective recommendations. Collaborative recommendation is probably the most familiar, most widely implemented and most mature of the technologies. Collaborative recommender systems aggregate ratings of objects, recognize commonalities between users on the basis of their ratings, and generate new recommendations.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
Recommendation systems are software programs that help a user to find products according to their needs and interests by using the user’s rating of each item and the user’s preferences. Recommendation systems are ubiquitous. If you’ve ever looked for movies on Netflix or glanced through posts on Instagram, you’ve used a recommendation system without even knowing it. With online shopping, customers have nearly countless options. Nobody has the time to browse through infinite number of pages in order to search for a product or purchase a product. These types of algorithms lead to service enhancement and customer satisfaction which in turn, brings in more traffic onto the website. Recommendation systems play an important role in helping users find products and content they care about (Towardsdatascience, 2021).
The an Online Recommendation System Using Collaborative Filtering would help the user purchase a book, by recommending books based on collaborative filtering and association rule mining. Collaborative filtering imitates user-to-user and item-to-item type of recommendations. It forecasts users’ preferences as a linear, weighted permutation of other user’s preferences. Association rule mining is a technique which intends to detect recurrently occurring patterns, correlations, or associations from datasets found in different kinds of databases such as relational databases, transactional databases, and other forms of data warehouses.
The challenges encountered that led to the execution of the research work is that, a lot of researchers depend on human ratings for books before acquiring theirs. It is against the background that the developments of this software to enable researcher to get high rated books with ease, thereby making optimum recommendation of impactful books. This research work is based on providing adequate information about books. At the end of this project, the research work, the software will be able to recommend high rated and reviewed books that have been of help to other researchers with reference to their research goal.
1.3 Statement of Problems
Investigation reveals the problems of the existing An Online Recommendation System Using Collaborative Filtering, which are:
- A lot of people depend on human ratings for books.
- Individuals do read and rely on the book reviews before making book acquisition decision
- The manual process of sourcing for information from books is time consuming.
1.4 Aim and Objectives of Study
The aim of the study is to design and implement a software that will recommend high rate and most reviewed book with furtherance to the research topic. In achieving this aim, the following specific objectives were laid out as follows:
- To develop a software that will save the precious time of customer and very efficient to use.
- To design a system that will provide large number of choices for books and also recommend for books.
- To design system that will enable user to buy book easily by making online payment.
- To design a system recommending algorithm scale well with co-rated items.
1.5 Significance of Study
This study will be of immense benefit to Students, Doctors, Engineers and other researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.
1.6 Scope of Study
The study focuses on the Design and Implementation of An Online Recommendation System Using Collaborative Filtering in Federal Polytechnic Nekede Library.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).
1.8 Definition of Terms
Recommender: a recommender is a word used to portray someone who puts forward (something or someone) as being suitable for a particular purpose or role.
Machine Learning: This is a field in artificial intelligence (AI) focuses on calculations (algorithms) that enable computer systems to learn.
Algorithm: An algorithm is a self-contained succession of activities to be performed with a specific end goal to tackle a particular problem.
Collaborative Filtering: Collaborative filtering (CF) is a technique applied mainly in recommendation systems to make automatic predictions about the interests of a client by gathering inclinations or taste data from numerous clients (collaboration).
System: This is a set of interacting or interdependent component parts forming a complex or intricate whole.
Book: A book is a set of written, printed, illustrated, or blank sheets, made of paper, parchment, or other materials, fastened together to hinge at one side, with text and/or images printed in ink.
Bookshop: A bookshop is a location where books are stocked for the purpose of being rented or purchased.
Online Bookshop: An online bookshop is an internet website that runs an e-commerce service where online users can access information about books, buy or rent books whenever their timing is ideal.
