Recommender Systems

ITS 632 Module Seven Essay Guidelines and Rubric

Topic: Recommender Systems

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.

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. The UC Library is a good place to find these sources. Be sure to cite and reference your work using the APA guides and essay template that are located in the courseroom.

Include the following critical elements in your essay:

I. 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.

II. Contrast with traditional systems: Please explain how a recommender system differs from a typical classification or predictive modeling system.

III. 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.

Required elements:  Please ensure your paper complies APA 6th edition style guidelines. There is an essay template located under the Information link.  APA basics:

o Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″) o Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin o Use 12 pt. Times New Roman font o Include an introduction and a conclusion (at least one paragraph)

 Follow the outline provided above and use section headers to improve the readability of your paper. If I cannot read and understand it, you will not earn credit for the content.

Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value

Recommender systems

Described the purpose of recommender systems. Explained the difference from customer or product-based system.

Described the purpose of recommender systems. Did not explain the difference from customer or product-based system. Explanations lacked sufficient detail.

Did not describe the purpose of recommender systems. Did not explain the difference from customer or product-based system.

Contrast with traditional systems

Explained how a recommender system differs from a typical classification or predictive modeling system

Did not sufficiently explain how a recommender system differs from a typical classification or predictive modeling system. Explanations lacked sufficient detail.

Did not sufficiently explain how a recommender system differs from a typical classification or predictive modeling system.

Collaborative filtering

Outlined one method of collaborative filtering. Discussed 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.

Explanations lacked sufficient detail. Outlined one method of collaborative filtering Did not discuss why it works in the context of recommender systems and describe what its limitations are in practice. Did not describe a modern technique/system available to overcome these limitations.

Did not outline one method of collaborative filtering. Did not discuss why it works in the context of recommender systems and describe what its limitations are in practice. Did not describe a modern technique/system available to overcome these limitations.

Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization.

Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas.

Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas.

EARNED TOTAL 100%

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