ElectricDeel Data Collec*on And Product Recommenda*on Engine For Consumer Electronics
Senior Project Poster Day 2011 – Department of Computer and Informa*on Science – University of Pennsylvania
The Web
Crawler
DistributedFile
System
DataProcessing
Pipeline
Database
Web Application
User
User
User
User
Recommendation Algorithm
Architecture
Design Goals 1. Accuracy • Recommendation algorithm must accurately reflect user preferences
• Data must be comprehensive and error-‐free 2. Ease of Use • Must be powerful enough to be worth using • Must be simple enough for Grandma
3. Transparency • Recommendations should not be a black box – users need to understand why products were recommended
4. Scalability and Modularity • It should be easy to add new product types and data sources
• Today: TV recommendations, Newegg data • Tomorrow: Laptop and cell phone recommendations, Amazon and BestBuy data
Eddie Siegel, Vikas Shanbhogue, Bennett Blazei Advisor: Zachary Ives
User Interface 1
1
2
3
Questions are simple and jargon-‐free.
2 The priorities that are determined by the recommendation algorithm are shown. If the user disagrees with the results, they can click and drag to rearrange them.
3 Results are shown as a score from 0-‐100 along with the cheapest price available. Clicking on a result provides more info.
Recommenda8on System
Crawler
Mo8va8on • Shopping for consumer electronics is hard • Shoppers do the same research repeatedly • No simple way to find the perfect product
BAD “What resolution should it have?” GOOD “What are you going to use it for?”
Overview ElectricDeel makes it easier to shop for consumer electronics by asking consumers everyday, non-‐technical questions instead of forcing them to decipher a complex list of technical specifications.
User Feature Preferences Similar Feature Trade-‐off
• User input is used to build a set of weights • Weights describe the relative importance of
product attributes for a particular user • Weights are used to calculate a score for
each product
