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Ferosa - Insights
- 1. FeRoSA F a c e t e d R e c o m m e n d a t i o n S y s t e m f o r S c i e n t i f i c A r t i c l e s
- 3. Scientific ArticlesA C L A n t h o l o g y – A c o l l e c t i o n o f 2 0 , 0 0 0 a r t i c l e s i n c o m p u t a t i o n a l l i n g u i s t i c s
- 4. FacetedN o t j u s t r e c o m m e n d a t i o n s , b u t h o w t h e y a r e r e l a t e d
- 5. www.ferosa.orgL i v e a n d r u n n i n g
- 6. •Edge labelling task b d l A b A d l • Set of Nodes • Links between similar nodes • Label the edges • Analogy • Nudge user – suggest why one should buy the combo offered in Flipkart • Type of social ties in a friendship network
- 7. CHALLENGES Quality Accessibility Ranking Scalable Q R A S • High Specificity & Precision • Outperforms current system for Scientific Articles retrieval by high margin • Individual ranking per facet • Most relevant entry comes first • Aggregation of ranklists over Content and Citation network info • Categorized into 4 facets • Easy to streamline as per need and filter results • Random Walks (with restarts) • Independent of domain
- 8. InformationOverload Even for Relatively closed community like ACL IRTools Rather than text based indexing Varyingintentions Streamlined results based on intention, entries may appear, which otherwise may not appear in flat recommendations
- 10. Dataset ACL Anthology Collection Statistics Full Filtered Number of papers 21,212 9,843 Average number of references (within ACL only) 5.23 6.21 Number of unique authors 17,551 7,892 Number of unique venues 451 280 • Computational Linguistics • 1961 – 2013 • text data open to public
- 11. FormCitationNetwork • Identify Citation Contexts and Section heading - parscit • Section heading to Facet Mapping • Refinement of facets from prior works Number of citation contexts extracted 61,051 Number of BG Edges 23,022 Number of AA Edges 10,797 Number of MD Edges 8,828 Number of CM Edges 18,404 AA – Alternative Approaches BG – Background CM – Comparison MD – Method
- 12. InducedSubgraphs • Query Paper • 2 hop citation in either direction • Highly similar papers based on cosine similarity Nodes • Edges belonging to a particular facet • 4 different subgraphs for each query paperEdges
- 13. RandomWalks • Random walks with restarts • The walker iteratively moves to its neighbourhood with a probability proportional to the edge weights. • Restart probability c = 0.4, to return to the starting node i. • Teleportation with probability 0.3
- 14. RankAggregation Aggregation of ranked lists based on Content similarity RWR Values R package Optimization problem Spearman footrule
- 15. EXPERIMENTAL RESULTS • most cosine similar paper comes in 1 hop or 2 hop itself • less edge density as citation increases (due to single edges or few edges) • MD sub-graphs have nodes with high degree • Average path length increases with citation count • clustering coefficient correlates wit edge density • 1-hop nodes contribute more in this measurement.
- 16. EVALUATION FeRoSA Google Scholar Microsoft Academic Search LDA based system (Liang et.al, 2011)
- 17. EVALUATION
- 18. EVALUATION • All systems perform better in >2 hop • cosine similarity - FeRoSA works in all sections, while others works marginally better or equivalent to ferosa only in high or mid • Pr, - FeRoSA in all 3 buckets, others suffer in low citation buckets
- 19. Scalable solution High specificity Stratification Flat recommendation Multi-hop neighbors Low citation buckets
- 20. THANKS