A content based approach to collaborative filtering
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A Content-Based Approach to Collaborative Filtering. Brandon Douthit-Wood CS 470 – Final Presentation. Collaborative Filtering. Method of automating word-of-mouth Large groups of users collaborate by rating products, services, news articles, etc.

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A Content-Based Approach to Collaborative Filtering

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A content based approach to collaborative filtering

A Content-Based Approach to Collaborative Filtering

Brandon Douthit-Wood

CS 470 – Final Presentation


Collaborative filtering

Collaborative Filtering

  • Method of automating word-of-mouth

  • Large groups of users collaborate by rating products, services, news articles, etc.

  • Analyze ratings data of the group to produce recommendations for individual users

    • Find users with similar tastes


Problems with collaborative filtering methods

Problems with Collaborative Filtering Methods

  • Performance

    • Prohibitively large dataset

  • Scalability

    • Will the solution scale to millions of users on the Internet?

  • Sparsity of data

    • User who has rated few items

    • Item with few ratings


Problems with collaborative filtering methods1

Problems with Collaborative Filtering Methods

  • Cannot compare users that have no common ratings

(Ratings on a scale of 1-5)


A content based approach

A Content-Based Approach

  • Build a feature list for each user based on content of items rated

  • Compare users’ features to make recommendations

  • Now we can find similarity between users with no common ratings


Data source

Data Source

  • EachMovie Project

    • Compaq Systems Research Center

    • Over 18 months collected 2,811,983 ratings for 1,628 movies from 72,916 users

    • Ratings given on 1-5 scale

    • Dataset split into 75% training, 25% testing

  • Internet Movie Database (IMDb)

    • Huge database of movie information

      • Actors, director, genre, plot description, etc.


Creating the feature list

Creating the Feature List

  • Retrieve content information for each movie from IMDb dataset – create “bag of words”

  • Throw out common words (i.e.: the, and, but)

  • Calculate frequency of remaining words, create movie’s feature list

    • Frequencies weighted based on total number of terms


Comparing users

Comparing Users

  • Each user has positive and negative feature list

    • Combine feature lists of movies they have rated

  • Compare user’s feature lists using Pearson Correlation Coefficient

  • Users can be compared with no common ratings

  • Able to recommend items with few ratings

  • Users only need to rate a few items to receive recommendations


Methods

Methods

  • Three methods attempted to improve performance:

    • Clustering of users

    • Random groups of users

    • Compare users directly to items


User clustering

User Clustering

  • Simple algorithm, starting with first user:

    • Compare to existing clusters first

      • If similarity is high, merge user into cluster

    • Compare to each remaining user

    • Stop if correlation is above threshold

    • Once a similar user is found, create a new cluster from the two users

      • Cluster has combined feature list of all its users

  • Not as efficient as possible - O(n2)


User clustering1

User Clustering

  • Once clusters are formed, we can predict ratings for each item

    • For each user, find their 10 nearest neighbors

    • Predicted rating is the average rating of item from these neighbors


Selecting a random group

Selecting a Random Group

  • Randomly select 5000 users as a (hopefully) representative sample

  • As before, find a user’s 10 nearest neighbors from the random group

    • Predicted rating is the average rating of item from these neighbors

  • Much less work than clustering

    • How much accuracy (if any) will be lost?


Comparing users to items

Comparing Users to Items

  • No collaborative filtering involved

  • Compare the positive and negative feature lists of user to feature list of item

    • Make prediction based on which feature list has higher correlation with item

  • Pretty quick and easy to do

    • How accurate will this be?


Analyzing predictions

Analyzing Predictions

  • Collected 3 metrics to evaluate predictions

    • Accuracy: all items predicted correctly

    • Precision: positive items predicted correctly

    • Recall: unseen positive items predicted correctly

  • Precision and recall have inverse relationship


Results

Results


Conclusions

Conclusions

  • Large gain from clustering users

    • Is the extra work worth it?

    • Depends on the application

  • Purely content-based predictions worked pretty well

    • Simple, fast solution

  • Random group prediction also performed reasonably well

  • Problems solved by content-based analysis:

    • Sparsity of data

    • Performance

    • Scalability


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