Recommending documents using Feature Guided Automated Collaborative Filtering - PowerPoint PPT Presentation

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Recommending documents using Feature Guided Automated Collaborative Filtering

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Recommending documents using Feature Guided Automated Collaborative Filtering Gabriela Pol icov , Pavol N vrat Department of Computer Science and Engineering ... – PowerPoint PPT presentation

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Title: Recommending documents using Feature Guided Automated Collaborative Filtering


1
Recommending documents using Feature Guided
Automated Collaborative Filtering
Gabriela Polcicová, Pavol Návrat
  • Department of Computer Science and Engineering,
    Faculty of Electrical Engineering and Information
    Technology, Slovak University of Technology
  • polcicova_at_dcs.elf.stuba.sk

2
Overiew
  • Information Filtering
  • Recommender system
  • Limitations and open problems
  • Conclusions

3
Information Filtering
  • Information Filtering
  • delivery of relevant information to the people
    who need it
  • Content-based Filtering
  • recommending documents based on content and
    properties of document
  • Automated Collaborative Filtering
  • making recommendations using opinions of users
  • Featute Guided Automated Collaborative Filtering
  • making recommendations using opinions of users
    accordig to categories

4
Example of Information Filtering
Feature Guided Automated Collaborative Filtering
Collaborative Filtering
5
Example of Information Filtering
Feature Guided Automated Collaborative Filtering
Collaborative Filtering
6
Example of Information Filtering
Feature Guided Automated Collaborative Filtering
Collaborative Filtering
7
Recommender System
  • collects users opinions
  • finds like-minded users
  • makes recommendation

8
Communication Agent
WWW
HTML-documents
Ratings
CA
URL-category
List of recom- mendations
Request for profile registration
User
List of recommen- dations
URL- -category
List of ratings
Request for profile registration
Re- commen- dations
Profile
Server
Yahoo!
URL-category
9
Communication Agent
WWW
HTML-documents
Ratings
CA
URL-category
List of recom- mendations
Request for profile registration
User
List of recommen- dations
URL- -category
List of ratings
Request for profile registration
Re- commen- dations
Profile
Yahoo!
Server
URL-category

by the system
Category is determined
by the user
Rating URL, value, category
Recommendation URL, predicted value, category
10
Communication Agent
WWW
HTML-documents
Ratings
CA
URL-category
List of recom- mendations
Request for profile registration
User
List of recommen- dations
URL- -category
List of ratings
Request for profile registration
Re- commen- dations
Profile
Yahoo!
Server
URL-category
List of predictions and like-minded users
List of ratings and found like-minded users
List of recommen- dations
Profiles of like-minded users
List of profiles
RA
List of profiles
Profiles of other users
11
Limitations
  • Collaborative filtering
  • critical amount of rated documents is required
  • recommends documents rated by at least one user
  • Documents categorization
  • categorization requires users interaction
  • user can not change assigned category

Open Problem
  • Using past predictions in computing correlations
  • how much can this help?

12
Conclusions
  • Preliminary results
  • recommendations are made by users agents
    independently
  • in addition to ratings, previously computed
    predictions are used
  • Future work
  • using predictions from content-based filtering

13
This method can be used
  • By a community of users that
  • communicate on Internet
  • use electronic web documents
  • rate documents

14
Degree of Similarity of Two Profiles for Each
Category
15
Prediction of the Rating
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