Entity Recommendations Using Hierarchical Knowledge Bases
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This document summarizes an approach to entity recommendations using hierarchical knowledge bases. It proposes using spreading activation theory on Wikipedia's category hierarchy to model user interests and generate recommendations. The approach transforms Wikipedia's category structure into a hierarchy and uses spreading activation to calculate interest scores for categories. These scores are then propagated across the hierarchy to score and rank entities. The approach is evaluated on movie recommendations and shows improved recall over baseline methods, particularly when incorporating category priority weights. Future work to improve normalization and validate category priorities is discussed.
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Entity Recommendations Using Hierarchical Knowledge Bases
- 1. Entity Recommendations Using Hierarchical Knowledge Bases Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (KNOW@LOD) at ESWC2015, May 31, 2015 Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton - OH
- 5. • Content Based (Users’ Ratings) – Types • Term based • Entity based • Knowledge based/Graph based – Provides the most personalized results • Collaborative (Other users’ ratings) • Hybrid (Both content and collaborative) – Works best Entity Recommendations
- 6. • Structured representation of relationships among concepts • Allows better understanding of the user interests • Cold start problem is addressed well 6 Knowledge Base Enabled Recommendations
- 8. Interstellar (film) Gravity (film) Relationships 8 Knowledge Base Enabled Recommendations Too many properties to address
- 9. Ball Games Football Games 9 Is-a Focus specifically on hierarchical relationships Hierarchical Relations
- 10. • Human memory has been argued to be structured as a hierarchy of concepts (Semantic Network) • Spreading activation theory has been utilized to simulate search on semantic network • This theory has not been well explored for user interest modeling and entity recommendations 10 Hierarchical Relations
- 11. • Domain Coverage • Crowd-sourced knowledge base –Regular updates –Driven by strict guidelines 11 Wikipedia
- 13. 13 Parent Categories Wikipedia Category Graph
- 14. 14 Spreading Activation Hierarchical Interest Graphs 1 2 3 3 4 Entity Un-rated by user 2 3 Reverse Spreading Wikipedia Category Hierarchy Approach
- 15. 15 Hierarchical Interest Graphs 1 2 3 3 4 Entity Un-rated by user 2 3 Reverse Spreading Approach Spreading Activation Wikipedia Category Hierarchy
- 16. Internet Semantic Search Linked Data Metadata Technology World Wide Web Semantic Web Structured Information 0.8 0.2 0.6 Scores for Interests 16 User Interests 0.7 0.5 0.4 0.3 Activation Function
- 17. • Wikipedia Category Structure is transformed to a hierarchy (Wikipedia Hierarchy) – 0.8 Million Categories with approx 2 Million relationships between them – Category “Main Topic Classification” is root of the hierarchy as it subsumes 98% of the categories – Each Category is assigned a Hierarchy level based on shortest path from root node • Spreading Activation Theory on Wikipedia Hierarchy • Experimented with different Activation Functions – Bell, Bell Log, Priority Intersect 17 Hierarchical Interest Graphs
- 18. Romance Films Titanic Anna_Karenina Forever Young Films Romantic Drama Films 1990s romantic drama films Structured Information 5 4 4 Ratings of Movies 18 Movies 4 3 3 1
- 19. 19 Spreading Activation Hierarchical Interest Graphs 1 2 3 3 4 Entity Un-rated by user 2 3 Reverse Spreading Wikipedia Category Hierarchy Approach
- 20. 1990s Romantic Drama Films Romantic Drama Films 4 3 20 Function to rank the entities based on category scores The Remains of the Day Jude 5 4 Ranking Unrated Entities
- 21. 21 The Terminator 1984 Films 1980s Action Films Films Directed by James Cameron Action Horror Films Robot Films 4 3 2 3 4 Reverse Spreading
- 22. • Sum the activation values of each parent of an entity 22 The Terminator 1984 Films 1980s Action Films Films Directed by James Cameron Action Horror Films Robot Films 4 3 2 3 4 16 Summing Sum
- 23. • Ranking is biased towards categories having larger number of entities • Example: – Category “English Language Films” has good interesting score for user – Results are topped by the entities connected to “English Language Films” for user 23 Summing – Drawbacks
- 24. • Parent category activation value is normalized by it’s out degree • Bias towards generic categories is addressed 24 The Terminator 1984 Films Footloose 3 1 1 1 Ghostbusters Degree Based Normalization Out Degree = 3 Spread Weight = 3/3 = 1
- 25. 25 1 2 3 4 Category Priority
- 26. • Priority of a category to an entity as edge weight 26 The Terminator 1984 Films Footloose 3 1 2 1 3 1.5 3 Ghostbusters Category Priority
- 27. Ai = Σ(Aj / (Oj * Pij)) 27 Reverse Spreading Activation Function Activation Value of the Entity i (Movie) Activation Value of the Categories of “i” Outdegree of Category j Priority of Category j to Entity “i”
- 28. • Focus on “5” rated movies for test as per Cremonesi et al. – New approach for evaluation of recommendation systems • Dataset - Movielens – Movies -- 3158 – Users -- 1476 – 0.9 m ratings • Baseline – Ziegler et al. – Hybrid(Content & Collaborative) technique to recommend books using Amazon book taxonomy – We compared with the content based part of Ziegler et al 28 Evaluation
- 29. 29 Summing Summing with Degree Normalization Summing with Degree Normalization and Priority Decay BellLog Top 1 -- 0.01 Top 20 -- 0.15 Top 1 -- 0.01 Top 20 -- 0.18 Top 1 -- 0.07 Top 20 -- 0.21 Bell Log Intersect Top 1 -- 0.0 Top 20 -- 0.09 Top 1 -- 0.01 Top 20 -- 0.20 Top 1 -- 0.07 Top 20 -- 0.24 Ziegler et al. Top 1 -- 0.0 Top 20 -- 0.002 Recall @Top 1 – Top 20
- 30. • While normalization is necessary, we are looking for better features for normalization • Priority works, however we find the category- entity priority added by users on Wikipedia needs a quality check • We are looking into collaborative Hierarchical Interest Graphs 30 Future Work
- 31. Paper at http://knoesis.org/library/resource.php?id=2150 Thank you Contact Siva Kumar (siva@knoesis.org) Pavan Kapanipathi (pavan@knoesis.org) @pavankaps Amit Sheth (amit@knoesis.org) @amit_p
- 32. 32 Top N Evaluation If: Rank(T) <= N Hits++ Else Miss++ Recall = Hits/Total User Ratings – Test Set Pick top rated entity ‘T’ Pick 1000 random entities Generate Rank for 1001 entities Cremonesi et al. Top N Evaluation