• Log In
  • Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • Home
  • Learn about MIS
    • Why MIS?
    • What is MIS?
    • Select a program
    • Scholarships
  • Programs
    • Overview
    • Bachelors in MIS
      • STEM Scholarships
    • Minor in MIS
      • Major or Minor?
      • MIS Minor
        • MIS Minor Declaration form
      • Digital Marketing Minor
      • Information Technology Innovation and Entrepreneurship Minor
      • Business Analytics Minor
    • Certificate in MIS
    • MS in IT Auditing & Cyber-Security
    • PhD in MIS
  • Current Students
    • IT Career Fair
    • Awards and scholarships
      • Annual MIS awards
      • Scholarships
      • Information Technology Awards
    • Professional achievement
      • Professional Achievement Program
      • Leaders
      • Earn points
      • Store
    • IBIT mentoring program
    • Independent study
    • Gradebook
    • Temple AIS
  • Alumni
    • Get involved with MIS
    • Register as an alum
    • Donate
  • Technology
    • Overview
    • About this site
      • Course Sites
        • Course Template
      • Doctoral and Master’s Student Sites
      • Faculty and Staff Sites
      • Account and site policy
    • MIS Project Server
  • Research
    • MIS Research
    • Seminars
  • About
    • About MIS
    • Faculty
    • Staff
    • Doctoral students
    • Student e-portfolios
    • Information Technology Advisory Board
    • Contact us and directions

Temple MIS

Connect and innovate with an elite information systems program

Fox School of Business
  • PRO!
  • Leaders
  • Members
  • Groups
  • Store
  • Earn Points
  • Newsletter

Research: Recommender Systems & Predictive Analysis

May 2, 2018

Recommender Systems; Predictive Data Analysis

“Recommender” or “Recommendation” systems refers to a type of information filtering that aims at predicting a users choice or outcome in a decision. These systems use various classification algorithms and techniques; such as decision trees for precise item choices or clustering for similarities it items to be chosen (which will be discussed shortly). Recommender systems are typically split into content-based or collaborative filtering systems:

  • Content based focuses upon “properties of the items recommended”[1]. An example being ‘recommending’ content to users such as Netflix surfing or Amazon.com shopping.
  • Collaborative filtering “recommend[s] items based on similarity measures between users and/or items”[1]. This type of recommender system is closely related to clustering.

Predictive Analytics stems from recommender systems which is “used to make predictions about unknown future events”[2]. This type of data analytics is anticipatory; relying on data mining and statistical techniques in order to develop these underlying insights. Predictive analytics allows for organizations to be much more forward-thinking and proactive in their business decision-making.

Recommendation system models relate closely to the topics of Decision Trees, Clustering, and Association Rule Mining that we have learned in Data Analytics (MIS2502). Multi-tiered decision tree analysis is used when recommending the best choice for an end user. Clustering is used in collaborative filtering methods in order to best group similar items with end users. Finally, association rule mining–as well as data mining in general–is used to best predict an end users ultimate choice.

As part of my job as a prospect research analyst, it is my job to determine who are the best candidates for outreach to support the school of medicine. If a recommender system algorithm were implemented, I would be able to more accurately determine which patients and doctors are more willing to contribute than others through logical decision-making analytics.

 

References

[1]Recommendation Systems, Chapter 9 – Stanford InfoLab n.d.. April 28 2018 http://infolab.stanford.edu/~ullman/mmds/ch9.pdf

[2]Imanuel. “What Is Predictive Analytics ?” Predictive Analytics Today, Bigtexts.com, 12 Apr. 2018, www.predictiveanalyticstoday.com/what-is-predictive-analytics/

Primary Sidebar

COMMUNITY ACTIVITY

Profile Photo
Ying Zhang
profile was updated
Profile Photo
Marissa Redline
received 50 points for MIS related work experience working for Temple University
Profile Photo
Ying Zhang
received 400 points for full-time MIS internship
Profile Photo
Ying Zhang
just received the Candidate badge
Profile Photo
Sean Simms
received 400 points for full-time MIS internship
Profile Photo
Sean Simms
just received the Apprentice badge
Profile Photo
Marissa Redline
received 100 points for part-time MIS internship
Profile Photo
Jingwen Lin
received 50 points for student worker
Profile Photo Profile Photo Profile Photo
Marissa, Ian, Baggio
received 25 points for Interesting Facts Page
Profile Photo
Baggio Bose
wrote a new post, Interesting Facts, on the site Baggio Bose
Profile Photo
Sean Simms
profile was updated
Profile Photo
Ian Marron
wrote a new post, Interesting Facts about Me, on the site Ian Marron
Profile Photo
Nik Fuchs
profile was updated
Profile Photo
Sean Simms
submitted their e-portfolio for approval
1 2 3 … 19 »

COURSES – FALL 2022

Footer

MANAGEMENT INFORMATION SYSTEMS

Fox School of Business
Temple University
210 Speakman Hall
1810 N. 13th Street
Philadelphia, PA 19122

ABOUT MIS

  • About
  • Why MIS?
  • Programs
  • Faculty
  • Staff
  • Give to MIS

MIS COMMUNITY

  • Members
  • Recent Activity
  • Sites
  • Groups

CURRENT STUDENTS

  • PRO
  • PRO Points
  • Leaders
  • Gradebook

CONNECT

RSSTwitterFacebookLinkedinFlickr

Copyright © 2022 Department of Management Information Systems · Fox School of Business · Temple University