recommender systems datamining subject
Essay Topic: Recommender Systems (Datamining subject)
Overview: The purpose of this assignment is to explore the processes associated with Recommender Systems.
Automated recommendations have become a pervasive feature of our online user experience, and due to their practical importance, recommender systems also represent an active area of scientific research. Along with the availability of new knowledge sources, including both structured and unstructured data that contain user-generated content, comes a steady stream of new systems that leverage such information to make better predictions. Recently, recommender systems have also emerged in the biomedical sciences and the objectives are the same in these applications, to predict ratings for missing items.
- Recommender systems. What is a recommender system? Describe the purpose and explain how this application works to help businesses serve their target market more effectively. Also, please explain the ways in which a recommender system differs from a customer or product-based system..
- Contrast with traditional systems. . Please explain how a recommender system differs from a typical classification or predictive modeling system. For example, logistic regression is perhaps the most widely used statistical model for classification. It is more preferable to CF because of the ensemble feature, ability to handle missing data, and it is generally robust to noise and outliers.
- Collaborative filtering (CF). Please outline one method of collaborative filtering. Please discuss why it works in the context of recommender systems and describe what its limitations are in practice. What modern techniques/systems are available to overcome these limitations? For example, memory-based algorithms can group every user with similar interests and identify the neighbors of a new user or currently active user to anticipate the preferences of new items that would be of interest.
- Recommender systems: Describe the purpose and explain how this application works to help businesses serve their target market more effectively. Also, please explain the ways in which a recommender system differs from a customer or product-based system.
- Contrast with traditional systems: Please explain how a recommender system differs from a typical classification or predictive modeling system.
- Collaborative filtering: Please outline one method of collaborative filtering. Please discuss why it works in the context of recommender systems and describe what its limitations are in practice. Described a modern technique/system available to overcome these limitations
Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings
Include all the following critical elements in your essay to get the full credit:
