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ReBoRK : A Review-based Book Recommender for K-12 Readers. Sole Pera CS 652-2012. Introduction. Existing book recommenders Make suggestions that match readers’ interests Problems:. One-size-fits-all. Required personal historical data may not always be available. - PowerPoint PPT Presentation
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ReBoRK:A REVIEW-BASED BOOK
RECOMMENDER FOR K-12 READERS
Sole PeraCS 652-2012
2
Introduction Existing book recommenders
Make suggestions that match readers’ interests
Problems:
Required personal historical data may not always be available
Suggest books without considering the reading ability of
its users
Existing Recommenders
Not personalized enough
One-size-fits-all
3
Proposed Solution ReBoRK
Review-based book recommender that generates personalized suggestions recommendations tailored towards K-12 students
Feature/ Opinion
Similarity
Metadata Similarity
Grade Level
Similarity
? ? ?
Item-Item Similarity
ReBoRK
4Information Extraction Anaphora Resolution
Use Guitar system to replace all discourse referents by their corresponding entities the reviews
Review Summarization Use [Pera et al, WISE `11] and SentiWordNet to
retain the portions of the reviews that express sentiment
Linguistic and Syntactic Analysis Use Stanford's NLP tools
Part-Of-Speech tagging Dependency parsing
Extraction Module
5Information Extraction Information Extraction Rules
Based on improvements upon the IE rules proposed in [Kamal et al., WIMS `12] Identify features on reviews Identify opinions on reviews Identify relationships between features and
opinions Example
Extraction Module
“It is written in simple rhyming patterns and illustrated with adorable pictures!”
Review for “Bear's New Friend” by Karma Wilson, extracted from Amazon.com
6Readability
User profile
Potential recommendations
Averaged Grade Level
Grade Level
?MatchedReading
Level
Recommendation Module
7Feature-Opinion Similarity
User profile
Potential recommendation
Feature-Opinion Distribution
Feature-Opinion Distribution
?Matched Preferenc
es
Recommendation Module
8Item-Item Similarity
User profile
Potential recommendation
<…, , , , , , …>
< …, , , , , , …> ?Matched
Bookmarking Patterns
Recommendation Module
9Content Similarity
User profile
Potential recommendation
Metadata: titles + descriptions
Metadata: title + description
?Matched Content
Recommendation Module
10
Item-Item
Readability
Feature/ Opinion
Metadata
Ranking
Top-10 Recommendati
ons
Fusion Strategies
Recommendation Module
11Proposed Validation Dataset
Metrics Precision Recall F-Measure
Extraction Module
12Proposed Validation Datasets
Validation Strategy N-fold cross-validation
Metrics Precision@K Mean Reciprocal Rank Normalized Discounted Cumulative Gain
(nDCG)
Recommendation Module
13Questions