ReBoRK : A Review-based Book Recommender for K-12 Readers

1. ReBoRK:A Review-based Book Recommender for K-12 Readers Sole Pera CS 652-2012

2. Introduction • Existing book recommenders • Make suggestions that match readers’ interests • Problems: One-size-fits-all Required personal historical data may not always be available Not personalized enough Existing Recommenders Suggest books without considering the reading ability of its users

3. Proposed Solution • ReBoRK • Review-based book recommender that generates personalized suggestions recommendations tailored towardsK-12students ? ? ? Grade Level Similarity Metadata Similarity Feature/ Opinion Similarity Item-Item Similarity ReBoRK

4. Information Extraction Extraction Module • 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

5. Information Extraction Extraction Module • 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 “It is written in simplerhyming patterns and illustrated with adorablepictures!” Review for “Bear's New Friend” by Karma Wilson, extracted from Amazon.com

6. Readability Recommendation Module User profile Averaged Grade Level ? Matched Reading Level Grade Level Potential recommendations

7. Feature-Opinion Similarity Recommendation Module User profile Feature-Opinion Distribution ? Matched Preferences Feature-Opinion Distribution Potential recommendation

8. Item-Item Similarity Recommendation Module User profile < …, , , , , , …> ? Matched Bookmarking Patterns <…, , , , , , …> Potential recommendation

9. Content Similarity Recommendation Module User profile Metadata: titles + descriptions ? Matched Content Metadata: title + description Potential recommendation

10. Ranking Recommendation Module Readability Item-Item Metadata Fusion Strategies Feature/ Opinion Top-10 Recommendations

11. Proposed Validation Extraction Module • Dataset • Metrics • Precision • Recall • F-Measure

12. Proposed Validation Recommendation Module • Datasets • Validation Strategy • N-fold cross-validation • Metrics • Precision@K • Mean Reciprocal Rank • Normalized Discounted Cumulative Gain (nDCG)

13. Questions