A Recommender System for
Exploratory Browsing
Team: SPARA
Tutor: Tommaso Di Noia
• Exploratory browsing
– Multiple domains
– Serendipitous
Following links
Content-based Recommender Systems
Recommender
System
User profile
…
Top-N Recommendations
Item1, 5
Item2, 1
Item5, 4
Item1...
The LOD Aspects
• Subgraph of DBpedia with different types of
items (Film, Person, Book) using SPARQL
– Rich set of featur...
USER INTERFACES
Bertin’s Visual Attributes
Bertin, Semiology of Graphics, 83 Slide: SheelaghCarpendale
Importance Ordering: Perceptual
Properties
Slide: Cecilia Aragon, HCDE, UW
Mackinlay, APT (A Presentation Tool), 1986
Movies Perspective
The Lost World:
Jurassic Park
The Lost World: Jurassic Park, is a
1997 American science fiction
adventu...
DEMO
FUTURE WORK
Parameter Tuning
Righteous Kill
starring
director
subject/broader
genre
Heat
RobertDeNiro
JohnAvnet
Serialkillerfilms
Dram...
Vector Space Model for LOD
+
+
+
… =
Slide: Tommaso Di Noia
Evaluate
• Experimenting with theαpparameters
– Learning αp
• Quality of the recommendations
– Discounted cumulative gain
...
• Evaluating the user interfaces
• Explanations
– Generating explanations
– Evaluating explanations
Thank You
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SPARA - A recommender system for exploratory browsing

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In the recent years we witnessed the rising of two interesting classes of user-centric applications: recommender systems and exploratory browsing tools.

On the one hand, recommendation systems show the user items that have a strong connection to his/her interests. On the other hand, by means of an exploratory browsing task the user is guided through the navigation of a knowledge space with the aim of finding new or serendipitous information.

With SPARA we took the best from the two approaches by developing a system that leverages the vast amount of data and knowledge encoded in DBpedia by taking the user preferences into consideration.

These systems can also allow the users to explore these recommendations by setting the focus on one of the recommended items and then showing further items that are related both to the user interests and to the item on focus. This exploratory process can lead to the discovery of items belonging to different categories/knowledge-domains. For example, a user browsing through a set of films can discover that one of them is based on a book. At this point, the user can click on the book, changing its current domain of interest and start browsing books instead. We developed a multi-domain recommender system that exploits the linked open data from DBpedia. This allows the system to identify both relations between items, and the category they belong to. We compute the recommendations using Jaccard similarity and Cosine similarity on the features extracted from DBpedia. We used visualization attributes from the Semiology field which enables encode the most important information in the most perceptually accurate way. Our approach allows multi-domain exploration of recommendations with a set of domains of interest.

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  • Dbpedia properties: Linked Data mitigates the limited content analysis issue
  • Importance Ordering: Encode the most important information in the most perceptually accurate way
  • Importance Ordering: Encode the most important information in the most perceptually accurate way
  • Tau: a measure of rank correlation, i.e., the similarity of the orderings of the data when ranked by each of the quantities.DCG: Using a graded relevance scale of documents in a search engine result set, DCG measures the usefulness, or gain, of a document based on its position in the result list.
  • SPARA - A recommender system for exploratory browsing

    1. 1. A Recommender System for Exploratory Browsing Team: SPARA Tutor: Tommaso Di Noia
    2. 2. • Exploratory browsing – Multiple domains – Serendipitous Following links
    3. 3. Content-based Recommender Systems Recommender System User profile … Top-N Recommendations Item1, 5 Item2, 1 Item5, 4 Item10, 5 …. Items Movie D Movie K Properties Director: Steven Spielberg Starring: Justin Bieber … …. CB-RSs recommend items to a user based on their description and on the profile of the user’s interests Item Rating Movie A 9/10 Actor J 5/10 Movie C 7/10 Movie A Slide: Tommaso Di Noia Recommendation Movie 1 Movie 2 Movie 3
    4. 4. The LOD Aspects • Subgraph of DBpedia with different types of items (Film, Person, Book) using SPARQL – Rich set of features - DBpedia properties associated to a type – Links to related items • Generate user profiles with ratings from IMDB – IMDB to DBpedia URI mapping – Inferring ratings for persons related to movies • Feed the data to the recommender system
    5. 5. USER INTERFACES
    6. 6. Bertin’s Visual Attributes Bertin, Semiology of Graphics, 83 Slide: SheelaghCarpendale
    7. 7. Importance Ordering: Perceptual Properties Slide: Cecilia Aragon, HCDE, UW Mackinlay, APT (A Presentation Tool), 1986
    8. 8. Movies Perspective The Lost World: Jurassic Park The Lost World: Jurassic Park, is a 1997 American science fiction adventure film directed by Steven Spielberg and the second of the Jurassic Park franchise. The film was produced by Gerald R. Molen and Colin Wilson. Director: Steven Spielberg Starring: Jeff Goldblum, Julianne Moore, Richard Attenborough, Vince Vaughn, Pete Postlethwaite
    9. 9. DEMO
    10. 10. FUTURE WORK
    11. 11. Parameter Tuning Righteous Kill starring director subject/broader genre Heat RobertDeNiro JohnAvnet Serialkillerfilms Drama AlPacino BrianDennehy Heistfilms Crimefilms starring RobertDeNiro AlPacino BrianDennehy Righteous Kill Heat … … Slide: Tommaso Di Noia
    12. 12. Vector Space Model for LOD + + + … = Slide: Tommaso Di Noia
    13. 13. Evaluate • Experimenting with theαpparameters – Learning αp • Quality of the recommendations – Discounted cumulative gain – Kendall tau rank correlation coefficient • Precision and Recall • Precision = true positive/ number of predicted positive • Recall = true positive / number of actual positive
    14. 14. • Evaluating the user interfaces • Explanations – Generating explanations – Evaluating explanations
    15. 15. Thank You

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