SPARA - A recommender system for exploratory browsing
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A Recommender System for Exploratory Browsing Team: SPARA Tutor: Tommaso Di Noia
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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.
Transcript of SPARA - A recommender system for exploratory browsing
A Recommender System for Exploratory Browsing
Team: SPARATutor: Tommaso Di Noia
• Exploratory browsing– Multiple domains– Serendipitous
Following links
Content-based Recommender Systems
Recommender System
User profile
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Top-N Recommendations
Item1, 5Item2, 1Item5, 4Item10, 5….
Items Movie D
Movie K
PropertiesDirector: Steven SpielbergStarring: Justin Bieber…
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CB-RSs recommend items to a user based on their description and on the profile of the user’s interests
Item RatingMovie A 9/10
Actor J 5/10
Movie C 7/10
Movie A
Slide: Tommaso Di Noia
RecommendationMovie 1
Movie 2
Movie 3
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
USER INTERFACES
Bertin’s Visual Attributes
Bertin, Semiology of Graphics, 83 Slide: Sheelagh Carpendale
Importance Ordering: Perceptual Properties
Slide: Cecilia Aragon, HCDE, UWMackinlay, APT (A Presentation Tool), 1986
Movies Perspective
The Lost World: Jurassic ParkThe 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 SpielbergStarring: Jeff Goldblum, Julianne Moore, Richard Attenborough, Vince Vaughn, Pete Postlethwaite
DEMO
FUTURE WORK
Parameter Tuning
Righteous Kill
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Heat
Robe
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Righteous KillHeat
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Slide: Tommaso Di Noia
Vector Space Model for LOD
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Evaluate
• Experimenting with the αp parameters– 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
• Evaluating the user interfaces• Explanations– Generating explanations– Evaluating explanations
Thank You
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