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Pros And Cons Of Content-Based Recommender System

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T is c Final Year Project Report A.I BASED E-COMMERCE RECOMMENDATION SYSTEM Supervisor: Dr. Aarij Mahmood Hussaan Group Members: • Abdul Wasay (16432) • Nabeel Asghar (16737) • Umer Waqar Ahmed (16514) Contents 1. Introduction 1.1. Background 1.2. What is Commerce 1.3. What is E-Commerce 1.4. E-Commerce vs. E-Business 2. What are Recommender Systems 3. Types of Recommender Systems 3.1. Collaborative Recommender System 3.2. Content-Based Recommender System 3.3. Knowledge-Based Recommender System 4. Our Preference Recommender System 4.1. Content-Based Recommender System 4.2. Working of Content-Based Recommender System 4.3. Pros & Cons of Content-Based Recommender System 5. Process of E-Commerce 6. Benefits of E-Commerce 7. Types …show more content…

Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. Recommender systems emerged into a rapidly expanding Internet business climate, and commercialization was almost immediate. These systems answer some very similar questions “Which digital camera should I buy? What is the best holiday for me and my family? Which movie should I rent? Which web sites will I find interesting? It is easy to expand this list with many examples in which people have to make decisions about how they want to spend their money or, on a broader level, about their future. The challenge of providing affordable, personal, and high-quality recommendations is central to the field and generates many interesting follow-up goals on a technical level. Because on the technical level, we are concerned with finding methods that exploit the available information and knowledge as effectively and efficiently as possible, user’s point of view must be considered when designing the end-user interaction processes. The design of these communication processes greatly influences the trust in the subsequent recommendations and ultimately in the decisions themselves. Users rarely act as rational agents who know exactly what they want. Even the way a recommender agent asks …show more content…

These A.I algorithms constitute as recommender systems, and are now a part of big ecommerce systems. Popular Recommender systems are: 6.1. Collaborative recommendation. 6.2. Content-based recommendation. 6.3. Knowledge-based recommendation. 7.1. Collaborative recommendation: The basic idea of these systems is that if users shared the same interests in the past – if they viewed or bought the same books, for instance – they will also have similar tastes in the future. For Example: user A and user B have a purchase history that overlaps strongly and user A has recently bought a book that B has not yet seen, the basic rationale is to propose this book also to B. Real life systems: Last.fm 7.2. Content-based recommendation: Content-based filtering methods are based on a description of the item and a profile of the user’s preference. In a content-based recommender system, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past. Real life systems: Pandora radio 7.3. Knowledge-based

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