Combining Content-based and Collaborative Filtering

1
Combining Content-based and Collaborative
Filtering
Gabriela Polcicová Pavol Návrat

• Department of Computer Science and Engineering,
  Slovak University of Technology
• polcicova_at_dcs.elf.stuba.sk
• navrat_at_elf.stuba.sk

2
Overview

• Information Filtering and its Types
• Combined Method
• Experiment with Information Filtering Methods
• Conclusions

3
Information Filtering (1)

• delivery of relevant information to the people
  who need it
• Types of Information Filtering
• Content-based - for textual documents
• Collaborative - for communities of users
• Interests
• information about interests - stored in profiles
• expressing opinions to documents - ratings
• Ratings i, j, rij
• for user i, item j, the value of rating rij

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Information Filtering (2)
Filter
Rated items user, item, value
Learning interests
Unrated items user, item
Estimating the value of rating
Recommendations user, item, estimation
Choosing recommendations
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Content-based Filtering (1)

• Basic idea
• recommending documents based on content and
  properties of document
• Profile
• consists of keywords with assigned weights
• only documents matching profile are recommended
• Recommendations
• based on objective measurable properties

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Content-based Filtering (2)
Documents rated by the user
Documents unrated by the user
Documents of interest
Documents, ratings
PROFILE
Keywords, phrases with weights
Documents matching profile gt recommended
documents
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Collaborative Filtering (1)

• Basic idea
• automating word of mouth
• leverage opinions of like-minded users while
  making decisions
• Schema
• collecting users opinions
• searching for like-minded users
• making recommendations

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Collaborative Filtering (2)
Profile of user 1
Profile of user 2
Profile of current user
Profile of user 3
Profile of user 4
Documents from like-minded users profiles gt
recommended documents
Profile of user 5
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Collaborative Filtering (3)

• Similarity measure Pearson Correlation
  Coefficient

? (rcj - rc) (rij - ri)
j ? Ici
kci
? (rcj - rc)2 ? (rij - ri)2
j ? Ici
j ? Ici

• Recommendations computation weighted sum of
  ratings

? (rij - ri) kci
i ? Ucj
rcj rc
? kci
i ? Ucj
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Combining Content-based and Collaborative
Filtering (1)

• Computing of estimates for missing ratings by
  Content-based Filtering method for each user
• Searching for like-minded users
• computing coefficient kci between current and
  i-th user (only from ratings)
• computing coefficient kci between current and
  i-th user (from both ratings and estimates)
• New recommendations computation
• using ratings (with coefficients kci) and also
  ratings with estimates (with coefficient kci) as
  weights in weighted sum of ratings and estimates

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Datasets for Experiments

• Data
• EachMovie - users ratings for movies
• www.research.digital.com/SRC/eachmovie/
• IMDB - textual information for CBF (movies
  descriptions)
• www.imdb.com/
• Datasets
• A - ratings from the period up to Mar 1, 1996
• (810 ratings from 71 users)
• B - ratings from the period uo to Mar 15, 1996
• (2407 ratings from 131 users)
• C - ratings from the period up to Apr 1, 1996
• (12290 ratings from 651 users)

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EachMovie Data and Constant Method

• Constant Method rcj 5

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Experiments with Combination of Content-based and
Collaborative Filtering (2)
Dataset
Content-based Filtering method
recommendations
test, training sets
Collaborative Filtering method
Apply filtering methods and evaluate their
performance
recommendations
Divide dataset into training set (90) and
test set (10)
test, training sets
Combined Filtering method
recommendations
test set
Constant method
recommendations
Evaluation of methods performance
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Metrics

• Coverage percentage of items for which the
  method is able to compute estimates
• Accuracy
• F-measure
• NMAE

R ?L R ?L L L
R - set of recommended items L - set of
liked items
2.Precision.Recall Precision Recall
R ? L R
Precision Recall
?rij - rij n.s
R ? L L
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Results of Experiments
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Conclusions

• Combination of content-based and collaborative
  filtering might help in initial phase

Future work

• Weighting of coefficients
• Comparing method with additional methods

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Content-based Filtering - Vector Representation
of Documents and Profiles
Documentj computer machine learning
W . Profile
Sim(W, Profile)
W . Profile
n
profilei ? rj .wij
j 1
TF-IDF
TF-IDF
TF-IDF
Wj (0, , 0, 0.5 , 0, , 0, 0.3
, 0, , 0, 0.2 , 0, , 0)
D ( , computer, , learning,
, machine, . )
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Collaborative Filtering - Example
A B C D E F G current 1
4 5 1 3 5 1 2 2
1 3
2 5 3 5
1 4 5 4 1
4 2 4 5 2 4 2 5
2
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Combining Content-based and Collaborative
Filtering (2)

• Similarity measure Pearson Correlation
  Coefficient

? (rcj - rc) (rij - ri)
CBF
CBF

j ? Ici

kci
? (rcj - rc)2 ? (rij - ri)2
CBF
CBF


j ? Ici
j ? Ici

• Recommendations computation weighted sum of
  ratings and estimates

? (rij - ri) kci ? (rij - ri) kci
CBF
i ? Ucj
i ? Ucj
rcj rc
? kci ? kci
i ? Ucj
i ? Ucj
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Experiments with Combination of Content-based and
Collaborative Filtering (1)

• Content-based Filtering Method (CBF)
• documents and profiles vector representation -
  weighted keywords (TF-IDF)
• estimation computation normalized dot product of
  document and profile vectors
• Collaborative Filtering (CF)
• Pearson correlation coefficient
• weighted sum of ratings
• Combination of CF and CBF
• Pearson correlation coefficients
• weighted sum of ratings and CBF estimations
• Constant Method (rcj 5)