Rui Yang, Wei Hu and Yuzhong Qu

Slides:

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Presentation transcript:

Using Semantic Web Technology to Improve Recommender Systems Based on Slope One Rui Yang, Wei Hu and Yuzhong Qu Department of Computer Science and Technology, Nanjing University, China

recommender systems A moive ? Recommender systems

Weighted Slope One(WSO) Customer item1 item2 item3 John 5 3 2 Mark 4 ? Lucy 1. The average difference in ratings between item 2 and 1 is (2+(-1))/2=0.5. Hence, on average, item 1 is rated above item 2 by 0.5. 2. The average difference between item 3 and 1 is 3. 3. Hence, if we attempt to predict the rating of Lucy for item 1 using her rating for item 2, we get 2+0.5 = 2.5. 4. If we try to predict her rating for item 1 using her rating of item 3, we get 5+3=8.

Problems 1. The user’s personality can not be covered in some especial cases. Example: Harry Potter phenomenon. 2. Traditional dataset: lack of useful attribute information about the items. (Especially at the beginning time)

Overview of the approach

1. Dataset identification DBpedia Freebase LinkedMDB DBTropes … A tvtropes.org wrapper, providing data about 1,700+ movies and 2,500+ tropes/features. Now about 10,000,000 RDF statements, 22,000 items, 22,000 feature types, and 1,750,000 feature instances.

Overview of the approach

2. Mapping RDF resources to items What methods we used? String matching TIP: Every film entity is identified by a dereferenceable URI like http://dbtropes.org/.../TheMovieTitle. It’s case by case For a book recommender system ------- ISBN For a paper recommender system ------- the paper titles, authors, journals and institutes (A VSM?)

Overview of the approach

3. LDSD computation What is LDSD?

3. LDSD computation What is LDSD?

3. LDSD computation What is LDSD?

3. LDSD computation Linked Data Semantic Distance What is LDSD? (Passant, A. AAAI 2010)

Overview of the approach

4. Improved Slope One scheme Weighted Slope One(WSO) Non-linear Transformation of WSO

4. Improved Slope One scheme Weighted Slope One(WSO) Linear Transformation of WSO

Evaluation Offline experiment User study Online experiment Nanjing University

Rating Prediction

Rating Prediction

Top-N Recommendation and Coverage

Future work More general mapping methods especially at the instance level New approaches for incremental semantic distance computation to support dataset update Integrate not only Slope One CF algorithm but also some sophisticated CF algorithms with Linked Data and Semantic Web technologies

Thank you Q&A