Project Abstract:
In this final year project we are going to develop a recommender system for an E-Commerce Store which will recommend the products to users which they may like. There are millions of products on E-Commerce stores and it is very difficult for a user to select a product. Therefore our recommender system will help customers by giving them the recommendations about the products of their interest. It will become easy for customers to buy products and of course he will like it and come back again. So by using a recommender system the business of E-Commerce store will increase.

Introduction:
Recommender Systems are basically systems which give us recommendations for items and products which might meet user’s interest. Facebook is a real world example in which users get recommendations for friends in form of people you may know. Also on youtube users get recommendation videos. Similarly our recommender system will recommend products to users of E-Commerce store.

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Importance of recommendation system:
Now a day’s people are so busy in their lives and daily routines that they cannot get time for even eating food. In this busy era people like to shop online. This trend is increasing day by day in Pakistan now. So if people go on online stores and they have millions of products in front them which will make them confuse therefore they could not shop properly. Our recommender system will help customers a lot to save their time by recommending them the relevant products that they may need or like. Recommender systems have a great role in most frequently used and exceedingly rated Internet sites like Amazon.com, Youtube.com, Yahoo, Trip Advisor, last.fm and IMDb.

Applications of recommender system:
Now a days recommender systems are being used in following domains:
E-Commerce: Here system recommend the products according to user’s interest and need.

Service: System recommend services like Hotel and Travel services.

Entertainment: This domain belongs to websites where system recommend movies and music etc.
Content: Recommend reading material like newspapers and books.

Social Networks: All types of systems that recommend something on social networking websites like facebook does by giving users friend suggestions.

Medical Informatics: Recommend treatment, also help in diagnosing the medical issues with patient like detecting tumors.

Advertising: Recommend ads to interested users of the products.

Related Work:
1.Intelligent Tourism Recommender System:
In 2014 1 author make a recommender system for tourists to customize their tours. It will recommend places based on their taste and restrictions using personalization techniques. This system automatically learned user’s preferences through their feedbacks. This system used automated planner to schedule the recommendations within a route which can span several days of the tourists. Clustering algorithms are used to classify tourists having same tastes. Reasoning procedure are used to deduced user’s preferences. The system also tell about the opening and closing timing of attractions.

2.The Youtube video recommender system:
In 2010 2 the author built a recommender system for Youtube videos recommendations. According to a survey in one minute users upload more than 24 hours of videos. Therefore there is huge need of recommendations. This system recommend videos to only signed in users based on their previous history. There will be limited recommendations for signed out users. Authors face some problems like sometimes quality of video is very poor therefore video corrupt in short span of time. And mostly on Youtube the duration of video is not so long so interaction is short and noisy. Authors classified data in two broad classes:1) content data. 2) user activity data on based of which the system did recommendations.
3.Recommender systems in e-commerce:
In 1999 3 authors built recommender systems for e-commerce stores. Amazon at that time sale only books and they use this recommender system which recommends books to customers on two basis 1) The frequently searched books by a user who purchase same book as you.2) and the books of author whose book you already purchased. eBay is an online store which create profiles of buyer and seller. On this website both buyers and sellers give ratings to each other and the recommender system recommend the buyer to seller and seller to buyer on the basis of these ratings.
Motivation and Scope:
Data science is an emerging field and it has appeared due to huge quantity of data. We use data science to manage this data, visualization, and manipulation of this large data. After this we can use Machine learning algorithms and techniques for recommendations and predictions.

The motivation to develop this Recommendation System is only to save the important time of people so that they can use it at some other task which is more important than shopping. Also due to such recommender systems people will prefer online shopping and the online business will increase.
Goals and Objectives:
We have following goals and objectives with our FYP:
Built a recommender system for an e-commerce store.

Explore the challenges in building a recommender system.

To explore techniques of machine learning used in recommendation systems.

To visualize data so that meaningful recommendations can be done.

Develop a recommender system which will help to solve the real life problems.

Explore how recommendation systems can help in enhancing the buisnesses.

Tools and Technologies:
Following tools and technologies will be used for our FYP project:
Python
Python libraries:
scikit-learn (aka sk-learn)
numpy
scipy
pandas
seaborn
matplotlib
RiVal (An open source toolkit for recommender system evaluation)
Surprise (A Python scikit for building, and analyzing (collaborative-filtering)
Recommender systems. Various algorithms are built-in, with a focus on rating
prediction)
References:
1
http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Borras%20-%20Tourism%20Recommender%20Systems%20-%202014.pdf2 https://www.researchgate.net/profile/Sujoy_Gupta2/publication/221140967_The_YouTube_video_recommendation_system/links/53e834410cf21cc29fdc35d2/The-YouTube-video-recommendation-system.pdf3 https://s3.amazonaws.com/academia.edu.documents/31095343/recommender-systems-e-commerce.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A;Expires=1530968304;Signature=FSOHy4gnTe5EL9Jmypb1FqbK7Zk%3D;response-content-disposition=inline%3B%20filename%3DRecommender_systems_in_e-commerce.pdf