automating readers’ advisory to make book recommendations for k-12 readers by alicia wood

Automating Readers’ Advisory to Make BookRecommendations for K-12 Readers

by Alicia Wood

Problem

• Existing book recommenders failed to offer adequate choices for K-12 readers

• Important to provide good reading material among K-12 students

• Not easy to find the right books for the right audience

Who cares?

• Parents – 32% of American 4th graders proficient in reading

• Children

Previous Work

• Previous book recommenders • Extract features, opinion, feature/opinion pairs

– Bootstrapping, NLP, ML, extraction rules, latent semantic analysis, statistical analysis, and information retrieval

• Information extraction approaches on product reviews

• Amazon• Require historical data• Require an ontology• Don’t consider readability level of users

Proposed Solution

• Rabbit• Multi-dimensional approach• No feedback from users• ABET (Appeal-based extraction tool)

Readers’ Advisory

• Offers materials of potential interest with “the help of knowledgeable and non-judgmental library staff”

• Based on:– Reasons behind preferences– Topical areas– Content descriptions– Appeal factors (pacing, description of characters,

tone, etc.)

Appeal Factors & Terms

ABET

• Extracts appeal-term descriptions of books from reviews available

• Imperative to properly associate appeal terms and appeal factors– pairs can be correctly extracted to generate

accurate appeal-term description for the book

Extraction Rules for ABET

Example

• SA = “The narrative of the book is dramatic”– Subject: narrative

• SB = “He creates believable characters”– Object: character (AF)

• If subject/object is an appeal factor, the word semantically linked to that subject/object is often an appeal term

• Rules 1 + 2

Example

• “The characters are not simple– Rule 4 – negation

ABET

• Creates the appeal-term description for a book applying the rules

• Frequency of occurrence– degree of significance

Rabbit

1. Analyze profile of a reader2. Determine readability level 3. Select books4. Compute ranking score

Candidate Books

• CB – candidate book available at a book repository Rep• PB each book in R’s profile• |P| - # of books in R’s profile• TRoLL(CB), TRoLL(PB) – grade level of CB/PB

determined by TRoLL

Topical Similarity Measure

• CB – vector of weights of CB if subject heading is of CB

• P – vector of weights of Pi (proportion between number of books in P that have been assigned Pi)

Content Similarity

• Enhanced version of cosine similarity

• CB = vector of Wcb1…Wcbn • P = vector of Wp1…Wpn• Wpi, Wcbi = weights of keywords Pi and Cb

Appeal Term Similarity

• F = set of appeal factors in appeal term descriptions

• CBf and Pf = n dimensional vector representation of appeal term distribution of an appeal factor (f)

Ranking Candidate Books

• Multiple linear regression

• Train using Ordinary Least Squares method– T set dataset

Experimental Results

1. Compute precision and recall of appeal factor-appeal term pairs extracted from book reviews

2. Analyze correctness of appeal-term descriptions created by ABET

3. Compare appeal-term descriptions generated by ABET with respect to ones extracted from Novelist

Experimental Results – 1

• 100 books • Manually annotated and compared with ABET• Precision: 0.85• Recall: 0.82• F-measure: 0.83• High accuracy for ABET in generating appeal

factor-appeal term pairs

Experimental Results - 2

• Surveys to determine correctness• Overall 94% accuracy

Experimental Results - 3

• Surveys to determine comparison• Appeal-term descriptions provided by ABET

favored over Novelist

Validation

• Computed empirical studies• Assessed performance of Rabbit using Eset in

terms of Normalized Discounted Cumulative Gain

• Rabbit locates more relevant books• Rabbit outperforms GoodReads and Novelist