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![Background
2
Linked Open Data (LOD) provides
domain knowledge and
rich Information about items
content-based recommender systems
[source]: http://lod-cloud.net](https://image.slidesharecdn.com/wise2017-171008191007/75/WISE2017-Factorization-Machines-Leveraging-Lightweight-Linked-Open-Data-enabled-Features-for-Top-N-Recommendations-2-2048.jpg)
![• 1st class citizen in LOD cloud
• Structured information from Wikipedia
• 4.58 million things
• 1,445,000 persons, 87,000 films etc.
Background
3
Linked Open Data (LOD) provides
domain knowledge and
rich Information about items
[source]: http://lod-cloud.net
knowledge
base](https://image.slidesharecdn.com/wise2017-171008191007/75/WISE2017-Factorization-Machines-Leveraging-Lightweight-Linked-Open-Data-enabled-Features-for-Top-N-Recommendations-3-2048.jpg)
![(Some) Related Work
• Semantic Similarity/Distance Measures
• [Passant et al. ISWC’10, AAAI’10]
• [Piao et al. SAC’16]
• Graph-based algorithms such as PageRank
• [Musto et al. UMAP’16]
• [Nguyen et al. WWW’15]
• Machine learning approaches
• [Noia et al. RecSys’12], VSM + SVM classifier
• [Noia et al. TIST’16], semantic paths + learning-to-rank (SPRank)
6](https://image.slidesharecdn.com/wise2017-171008191007/75/WISE2017-Factorization-Machines-Leveraging-Lightweight-Linked-Open-Data-enabled-Features-for-Top-N-Recommendations-6-2048.jpg)
![(Some) Related Work
• Semantic Similarity/Distance Measures
• [Passant et al. ISWC’10, AAAI’10]
• [Piao et al. SAC’16]
• Graph-based algorithms such as PageRank
• [Musto et al. UMAP’16]
• [Nguyen et al. WWW’15]
• Machine learning approaches
• [Noia et al. RecSys’12], VSM + SVM classifier
• [Noia et al. TIST’16], semantic paths + learning-to-rank (SPrank)
7
user-item
interactions
item background
knowledge
build a graph
extract features
feed to algorithms
SPARQL Endpoint](https://image.slidesharecdn.com/wise2017-171008191007/75/WISE2017-Factorization-Machines-Leveraging-Lightweight-Linked-Open-Data-enabled-Features-for-Top-N-Recommendations-7-2048.jpg)
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WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations
This document presents a novel approach using factorization machines combined with lightweight linked open data features for top-n recommendations. It highlights the use of domain knowledge from DBpedia and empirically compares the performance of its proposed method (lodfm) against various baseline recommendation algorithms. The study demonstrates that using lightweight features directly from a public SPARQL endpoint enhances recommendation accuracy without the need for extensive graph maintenance.
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WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations
- 1. Factorization Machines LeveragingLightweight Linked Open Data-enabled Features for Top-N Recommendations Guangyuan Piao, John G. Breslin Insight Centre for Data Analytics, National University of Ireland Galway The 18th International Conference on Web Information Systems Engineering Moscow, Russia, 7-10th, October
- 2. Background 2 Linked Open Data(LOD) provides domain knowledge and rich Information about items content-based recommender systems [source]: http://lod-cloud.net
- 3. • 1st classcitizen in LOD cloud • Structured information from Wikipedia • 4.58 million things • 1,445,000 persons, 87,000 films etc. Background 3 Linked Open Data (LOD) provides domain knowledge and rich Information about items [source]: http://lod-cloud.net knowledge base
- 4. Background Knowledge fromDBpedia 4 Chase_films Auto_racing_films…
- 5. • Knowledge isrepresented as SPO triples • SPO: Subject ! Property ! Object • Knowledge is freely accessible via a public SPARQL Endpoint Background Knowledge from DBpedia 5 musicComposer (Subject) (Property) (Object)
- 6. (Some) Related Work • Semantic Similarity/Distance Measures • [Passant et al. ISWC’10, AAAI’10] • [Piao et al. SAC’16] • Graph-based algorithms such as PageRank • [Musto et al. UMAP’16] • [Nguyen et al. WWW’15] • Machine learning approaches • [Noia et al. RecSys’12], VSM + SVM classifier • [Noia et al. TIST’16], semantic paths + learning-to-rank (SPRank) 6
- 7. (Some) Related Work • Semantic Similarity/Distance Measures • [Passant et al. ISWC’10, AAAI’10] • [Piao et al. SAC’16] • Graph-based algorithms such as PageRank • [Musto et al. UMAP’16] • [Nguyen et al. WWW’15] • Machine learning approaches • [Noia et al. RecSys’12], VSM + SVM classifier • [Noia et al. TIST’16], semantic paths + learning-to-rank (SPrank) 7 user-item interactions item background knowledge build a graph extract features feed to algorithms SPARQL Endpoint
- 8. Combined Graph 8 Chase_films … user-item interactions itembackground knowledge build a graph extract features feed to algorithms SPARQL Endpoint
- 9. • Using lightweightLOD features from DBpedia • lightweight: directly obtained via SPARQL Endpoint • Lightweight LOD features • Property-Object list (PO) Proposed Approach: Features 9 user-item interactions item background knowledge SPARQL Endpoint dbr:The_Godfather dbr:Carlo_Savina dbo:knownFor dbr:Francis_Ford_Coppola dbr:The_Godfather_Returns dbc:Gangster_films dbo:series dbo:director dc:subject feed to algorithms
- 10. • Using lightweightLOD features from DBpedia • lightweight: directly obtained via SPARQL Endpoint • LOD features • Property-Object list (PO) • Subject-Property list (SP) Proposed Approach: Features 10 user-item interactions item background knowledge SPARQL Endpoint dbr:The_Godfather dbr:Carlo_Savina dbo:knownFor dbr:Francis_Ford_Coppola dbr:The_Godfather_Returns dbc:Gangster_films dbo:series dbo:director dc:subject feed to algorithms
- 11. • Using lightweightLOD features from DBpedia • lightweight: directly obtained via SPARQL Endpoint • LOD features • Property-Object list (PO) • Subject-Property list (SP) • PageRank score (PR) Proposed Approach: Features 11 user-item interactions item background knowledge SPARQL Endpoint dbr:The_Godfather dbr:Carlo_Savina dbo:knownFor dbr:Francis_Ford_Coppola dbr:The_Godfather_Returns dbc:Gangster_films dbo:series dbo:director dc:subject feed to algorithms
- 12. • Factorization Machines(FMs) • Optimization: Bayesian Personalized Ranking (BPR) Proposed Approach: Algorithms 12
- 13. Proposed Approach 13 1 0… 1 0 … 0.2 0.2 … 0.1 0 … 0.1 0 1 … 0 1 … 0.3 0.5 … 0 0.3 … 0.2 … … … … … … … … … … … … … user item PO SP PR 1 0 … x1 Feature vector x Target y x2 • Overall features for Factorization Machines
- 14. • Movielens datasetfor LOD-enabled recommender systems • 80% for training set, and 20% for test set Experimental Setup: Dataset 14
- 15. • P@N: theprecision at rank N • R@N: the recall at rank N • nDCG@N: normalized Discounted Cumulative Gain • MRR: Mean Reciprocal Rank • MAP: Mean Average Precision Experimental Setup: Evaluation Metrics 15
- 16. • PopRank: baselineapproach • kNN-item: item-based k-nearest neighbors algorithm • BPRMF: matrix factorization with the BPR optimization • SPRank: learning-to-rank using semantic paths based on LOD • LODFM: our proposed approach Experimental Setup: Compared Methods 16
- 17. Results 17 best tuned parameters:m=200, PO+PR
- 18. Model Analysis: Features(m=10) 18
- 19.
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- 21. • LODFM providesstate-of-the-art performance • Using FMs with lightweight LOD-enabled features • directly obtained via a public SPARQL Endpoint of DBpedia • without maintaining graph, and extracting features from it • Useful features: Property-Object list & PageRank • Feature work • investigate other lightweight LOD-enable features • evaluate in other domain dataset Conclusions 21
- 22. 22 Guangyuan Piao e-mail: guangyuan.piao@insight-centre.org twitter:https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize