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Phd thesis final presentation

The document describes a PhD dissertation on linked data-based recommender systems. It presents an AlLied framework for executing and analyzing recommendation algorithms based on linked data. The framework includes implementations of graph-based and machine learning algorithms. An evaluation compares the performance of different graph-based algorithms using a user study on film recommendations. The results show that algorithms combining traversal and hierarchical approaches have the best balance of accuracy and novelty.

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Recommender Systems based on Linked Data PhD Dissertation Cristhian Figueroa PhD Maurizio Morisio PhD Juan Carlos Corrales Politecnico di Torino Universidad del Cauca March, 2017
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems RS: Software tools providing suggestions of resources to be of interest to a user 4 Recommender Systems (RS) Preprocessing Recommender Algorithm Ranking Input data Recommended resources Background data Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Large heterogeneous data on the Web -> Need for an automatic filtering tool
Linked Data based Recommender Systems Approach Technique Content-based Keywords, pixels, disk space etc. Collaborative Filtering Similar users’ preferences Knowledge-based Matches user requirements - resources features Hybrid Combines two or more approaches 5 Traditional RS approaches Problems of traditional RS: TScarcity: few ratings with regard to the total number of resources TCold start: new user/resource without ratings TNo standard data formats – low compatibility Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • Linked Data (LD): publishing and linking structured data on the Web • 1146 datasets (Jan 2017) [http://lod-cloud.net] • LD-based RS: LD -> knowledge about resources/users for recommendations from various domains. 6 Linked Data based RS Problems of traditional RS: TScarcity TCold start TNo standard data formats Linked Data based RS: RDo not require ratings data or user profile RStandardized access to data RMulti-domain knowledge R Richer resource representation R Semantic Analysis Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems • Kitchenham guidelines [kitchenham and Charters, 2007] • Main results: 1. Classification of Algorithms for LD based RS 2. Summary of the issues in current LD based RS 8 Systematic Literature Review (SLR) Published on CPE Journal: “A Systematic Review of Linked Data-based Recommender Systems” Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 9 SLR: 1. Algorithms Classification of algorithms for Linked Data-based RS Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 42,9% 20,0% 14,3% 8,6% 14,3% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Percentageofstudies Graph-based Machine Learning Memory based Probabilistic Others
Linked Data based Recommender Systems 10 SLR: 1. Algorithms Type Algorithms Advantages Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Graph-based • Semantic exploration - Dbrec[Passant,2010] - HyProximity[Stankovic et al,2012] - Page rank[Nguyen et al,2015] - VSM[Khrouf et al,2013] • Path based: - Spreading activation[Hajra et al,2014] - Random walk[Cantador et al,2011] - Vertex discovery[Strobin et al,2014] • Serendipitous Rec. • Explanations • Domain independent • Hierarchical data Machine Learning (ML) • Supervised - Classification - kNN[Ristoski et al,2013] - decision trees[Ostuni et al,2013] - RF[Musto et al,2014] - SVM[Kushwaha et al,2014] - Naïve bayes[Schmachtenberg et al,2014] - Bayesian classifier [Rabello Lopes et al,2014] • Unsupervised - Clustering - k-means[Manoj Kumar et al,2015] - Fuzzy-C means, SOM, PCA[Ostuni etal,2014] • Many algorithms already developed. • Large datasets. • Improve results with experience
Linked Data based Recommender Systems 11 SLR: 2. Current gaps of LD based RS Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Topic Issues Datasets • Local copy of LD datasets • Restricted access Manual Operations • Curated datasets – manual review • Selection of concepts for a domain Algorithms • Graph-based: - High cost for exploiting semantic features • ML: - No intrinsic semantic structure of the LD - Ratings and user’s profile information – cold start problem
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems • How to recommend resources considering the knowledge available on the web of data, analyzing their implicit relationships? 13 Research Question LD cloud Preprocessing Recommender Algorithm Ranking Input data Recommended resources Background data Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Recommendation -> semantic relationships from LD 2. Layered framework -> execute and test algorithms to create novel RS. 3. Graph-based algorithms in RS: more reliable -> intrinsic structure of the LD 4. ML algorithms in RS -> do not require the user’s profile information 14 Hypothesis Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems • A framework for executing and analyzing recommendation algorithms based on LD: AlLied • A selection of algorithms for RS -> knowledge of LD • Implementations: • Graph-based • ML 16 Approach Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 17 Conceptual architectures Recommender System Management User interface and applications Common Layers Knowledge Base Management Bottom Layer: structured data with user defined vocabularies Middle Layer: semantic web core Top Layer: user interface for applications Semantic Web Stack: (Horrocks et. Al, 2005) Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems Conceptual architectures Recommender System Management User interface and applications Common Layers Knowledge Base Management Conceptual architecture for applications on the web of data: (Heitman et. Al, 2012) RDF store Graph access Data homogenization service Data discovery service Graph query language Graph-based navigation interface Application logic Structured data authoring interface Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 18
Linked Data based Recommender Systems 19 AlLied Framework Steps of the recommendation process Resource generation Rec1 Rec2 Recn … Results grouping Rank1 Rank2 … Results ranking Rankn Presentation Recommender System Management User interface and applications AlLied framework Conceptual architectures Knowledge Base Knowledge Base Management Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Candidate Resource generation ranking Results grouping Results presentation Preprocessing
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 21
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset DBpedia Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 22 Preprocessing
Linked Data based Recommender Systems • Dbpedia: • Hierarchical schemas: • Wikipedia categories (SKOS) • Traversal relationships: • Direct and indirect relationships 23 Graph-based: knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 24 Candidate Resource generation Preprocessing
Linked Data based Recommender Systems • Traversal Generator: • Direct and indirect links - properties • Hierarchical Generator: • Distance in the category tree Graph-based: generation Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 25 The dark knight Man of steel American superhero films Batman film Batman begins dbpedia-owl:director Christopher Nolan dbpedia-owl:director Superhero films Superhero comedy films Megamind Initial Resource The mask Warner bros dbpedia-owl:distributor dbpedia-owl:distributor dbpedia-owl:distributor 1 2 3 3
Linked Data based Recommender Systems Hierarchical Generator Traversal Generator 26 Graph-based: generation: ReDyAl algorithm Initial Resource Traversal Generation CR>= MinRes LinksIR >= MinLinks Ranking Create category graph CR>= MinRes Update category graph CurrLevel >= MaxDist Y Y Y Y Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Hierarchical Generation currLevel++
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 27 Candidate Resource generation ranking Preprocessing
Linked Data based Recommender Systems • HyProximity: [Stankovic., et al, 2011] ℎ𝑦𝑃 𝑐%, 𝑐' = 𝑝 𝑐%, 𝑐' 𝑑 𝑐%, 𝑐' • Hierarchical measures: categories 𝑝 𝑐%, 𝑐' = 1 − 𝑙𝑜𝑔(ℎ𝑦𝑝𝑜 𝐶 + 1) 𝑙𝑜𝑔 𝐶 • Traversal measures: properties 𝑝 𝑐%, 𝑐' = −𝑙𝑜𝑔 𝑛 𝑀 • Linked Data Semantic Distance: [Passant, 2010] • Traversal distance: input/output direct and indirect links 𝐿𝐷𝑆𝐷 𝑐%, 𝑐' = 1 1 + 𝐶𝑑9:; + 𝐶𝑑<= +𝐶𝑖9:; +𝐶𝑖<= • Hybrid: • Traversal + hierarchical 28 Graph-based: ranking Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art distance weighting function
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 29 Candidate Resource generation ranking Results grouping Preprocessing
Linked Data based Recommender Systems • Meaningful broader category • Less common ancestor 30 Graph-based: results categorizer Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Batman begins American Superhero films Batman Films Man of steel Christopher Nolan Superhero films Superhero comedy films Megamind English directors The dark knight Neo noir films The mask Film noir Crime films Films genres Warner bros American film studios Entertainment
Linked Data based Recommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 31 Candidate Resource generation ranking Results grouping Results presentation Preprocessing
Linked Data based Recommender Systems • User study: • Domain: films • 109 students (PoliTo and Unicauca) • 45 queries (top films of IMDb) • Executed AlLied algorithms • Test List: merging top 10 films for each query and each algorithm. • Only top 20 results of the merged list • Measures: • RMSE: the square root of the mean of the square of all of the error. • Novelty: relevant recommended resources not known by the total number of resources 32 Graph-based: experimental setup Benchmarking website: http://natasha.polito.it/RSEvaluation/ Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems Graph-based: experimentation Bad performers High novelty Sweet spot Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Traversal + LDSD
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 35
Linked Data based Recommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset LODMatrix Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 36 Preprocessing Steps of the recommendation process
Linked Data based Recommender Systems • LODMatrix • Creation of a dataset tailored for ML T Need to know the application domain -> obtain only relevant data from that domain • Films from DBpedia • Data mining: quality of results can only be good as the input data [Paulheim., et al, 2012] • FDQ-KDT framework: [Corrales et al, 2015] • Address poor quality data in knowledge discover tasks. 37 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • LODMatrix 38 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art FDQ-KDT framework: [Corrales et al, 2015]
Linked Data based Recommender Systems • LODMatrix: • Attribute Selection: • Common features in state of the art RS: Discovery hub, LinkedMDB, SemMovieRec, Cinemappy, etc. • Matrix Generation: • All possible combinations of the attributes for each film. • 101000 films approx. (about 3 million instances) 39 ML: Knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • LODMatrix 40 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art FDQ-KDT framework: [Corrales et al, 2015]
Linked Data based Recommender Systems • LODMatrix: Data quality assessment: • DBpedia -> lot of missing and wrong data (outliers). • Local Outlier Factor: • local density given by the kNN • Tuckey’s method: • 1.5 Interquartile range • LODMatrix was depurated -> fixing data containing outliers • Automatic: Free web services -> fix erroneous data: • The Movie DB • OMDb • Manual fixing : tedious task. 41 ML: Knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 42 Candidate Resource generation Preprocessing
Linked Data based Recommender Systems ML: Generation Extract films in the same cluster as the query film Classification Model IR Query film Is it a new film? LODMatrix with cluster data Y Inferring cluster Candidate Resources N Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 43 • Generation phase: • Extracts films belonging to the same cluster as the IR -> reduces the search space • For new films uses the classification algorithm
Linked Data based Recommender Systems ML: Generation LODMatrix Clustering Classification Classification Model Training Data Cluster model Add clusters to lodmatrix Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 44 • Training phase: • Clustering: • Discover a set of clusters –> similar features • Classification: • New films (not in the LODMatrix) • Predicts the cluster based on attributes and a trained model.
Linked Data based Recommender Systems • Clustering: • K-means: most popular algorithm in RS based on LD: R Easy to use, fast algorithm and good quality clusters T k value selection • Selection of k value empirically: 2 – 100 clusters • Distance • Distribution • Density • K = 55 -> best values ML: Generation Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 45
Linked Data based Recommender Systems • Classification: Algorithms selected: most commonly used for RS in the state of the art. ML: Generation 13,22% 16,83% 75,67% 75,69% 0% 10% 20% 30% 40% 50% 60% 70% 80% kNN Naive Bayes Decision Tree Random Forest Classification Error (%) Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 46
Linked Data based Recommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 47 Candidate Resource generation ranking Preprocessing
Linked Data based Recommender Systems • Mixed Euclidean: R heterogeneous distance measure • Bregman Divergences: • Distance consistent with the NDCG [Acharyya et al,2012] • Itakura Saito, logarithmic loss, logistic loss, squared Euclidean and squared loss. R Easy to implement R Heterogeneous data R Execution time 48 ML: Ranking Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 49 Candidate Resource generation ranking Results grouping Results presentation Preprocessing
Linked Data based Recommender Systems • User study: • Used the lists generated in the user study for the graph based algorithms 50 ML: Experimental Setup AlLied • Measures: • Precision: fraction of retrieved resources that are relevant • Recall: fraction of relevant instances that are retrieved • F-Measure: harmonic mean between precision and recall Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art • Gold standard: • IMDb gold standard for films. • Top 10 films from IMDb for each query film
Linked Data based Recommender Systems 51 ML: User Study 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% Mixed Euclidean Generalized Divergence Itakura Saito Logarithmic Loss Logistic Loss Squared Euclidean Squared Loss Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 52 ML: Gold standard study 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% Mixed Euclidean Generalized Divergence Itakura Saito Logarithmic Loss Squared Euclidean Squared Loss Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems 54 Comparative evaluation: User Study 43,2% 20,0% 35,7% 22,0% 18,7% 9,9% 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% ReDyAl - Hybrid Ranker ML - Mixed Euclidean Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 55 Comparative evaluation: Gold standard 34,2% 24,2% 28,5% 20,1% 15,5% 11,0% 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% ReDyAl - Hybrid Ranker ML - Mixed Euclidean Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems • First SLR in LD-based RS • Allied: framework for testing and executing LD based RS • Implementation: graph-based and ML algorithms • ReDyAl: a dynamic algorithm for LD based RS • User and gold standard studies to assess accuracy • 14 research papers published during the PhD. • Directly related with the research topic: • 4 JCR journal papers • 2 conference papers (LNCS -Springer) • Others: 8 papers Contributions 57 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
Linked Data based Recommender Systems • AlLied • Dividing the recommendation process in meaningful phases help to: R Test and use different types of algorithms for each phase R Modular, extensible and reusable • Use of Semantic web standards R New RS with different configurations of algorithms R Reproducibility of results • ReDyAl: • Accurate and Novel recommendations Conclusions 59 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • ML and graph based algorithms for LD-based RS: • Graph-based implementation: • Advantage of the intrinsic relationships among data – graph structure of LD: R Better accuracy T Cannot deal with huge datasets • ML implementation: R Can recommend resources without user profile information R Deal with large amounts of data. R Classification -> new resources T Limited application domain T Datasets need to be adapted for ML • ML can be combined with graph-based algorithms to improve results. Conclusions 60 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • TellMeFirst: • Integration of ReDyAl and multisource • “Semantic Annotation and Classification in Practice” published on “IEEE ITPro Magazine” • Mobile application for films recommendation: • “ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data” presented on CBRecSys 2016 • Touristic recommender with Smart Spaces: • Associate touristic attraction with concepts of LD • “Linked Data-Driven Smart Spaces” presented on ruSMART 2014 • Multimodal Search of Business Processes: • Integration of the hierarchical generator -> hierarchical index for a BP repository. • “Improving Business Process Retrieval Using Categorization and Multimodal Search” published on “Knowledge-based Systems” journal Use cases 61 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems • More types of algorithms for the AlLied framework • Explanations of recommendations based on the knowledge of LD • Evaluation of accuracy and performance for algorithms. • Determine best algorithms for each component of the AlLied framework. • Personalized recommendations with LD Future works 62 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
Linked Data based Recommender Systems 63 Acknowledgements • COLCIENCIAS • PhD grant • Politecnico di Torino • SoftEng group • Universidad del Cauca • VRI • Telematics Engineering Group
Thanks!!! Recommender Systems based on Linked Data Cristhian Figueroa cristhian.figueroa@polito.it cfigmart@unicauca.edu.co PhD Maurizio Morisio PhD Juan Carlos Corrales Politecnico di Torino Universidad del Cauca

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Phd thesis final presentation

  • 1.
    Recommender Systems based on LinkedData PhD Dissertation Cristhian Figueroa PhD Maurizio Morisio PhD Juan Carlos Corrales Politecnico di Torino Universidad del Cauca March, 2017
  • 2.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 3.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 4.
    Linked Data basedRecommender Systems RS: Software tools providing suggestions of resources to be of interest to a user 4 Recommender Systems (RS) Preprocessing Recommender Algorithm Ranking Input data Recommended resources Background data Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Large heterogeneous data on the Web -> Need for an automatic filtering tool
  • 5.
    Linked Data basedRecommender Systems Approach Technique Content-based Keywords, pixels, disk space etc. Collaborative Filtering Similar users’ preferences Knowledge-based Matches user requirements - resources features Hybrid Combines two or more approaches 5 Traditional RS approaches Problems of traditional RS: TScarcity: few ratings with regard to the total number of resources TCold start: new user/resource without ratings TNo standard data formats – low compatibility Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 6.
    Linked Data basedRecommender Systems • Linked Data (LD): publishing and linking structured data on the Web • 1146 datasets (Jan 2017) [http://lod-cloud.net] • LD-based RS: LD -> knowledge about resources/users for recommendations from various domains. 6 Linked Data based RS Problems of traditional RS: TScarcity TCold start TNo standard data formats Linked Data based RS: RDo not require ratings data or user profile RStandardized access to data RMulti-domain knowledge R Richer resource representation R Semantic Analysis Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 7.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 8.
    Linked Data basedRecommender Systems • Kitchenham guidelines [kitchenham and Charters, 2007] • Main results: 1. Classification of Algorithms for LD based RS 2. Summary of the issues in current LD based RS 8 Systematic Literature Review (SLR) Published on CPE Journal: “A Systematic Review of Linked Data-based Recommender Systems” Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 9.
    Linked Data basedRecommender Systems 9 SLR: 1. Algorithms Classification of algorithms for Linked Data-based RS Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 42,9% 20,0% 14,3% 8,6% 14,3% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Percentageofstudies Graph-based Machine Learning Memory based Probabilistic Others
  • 10.
    Linked Data basedRecommender Systems 10 SLR: 1. Algorithms Type Algorithms Advantages Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Graph-based • Semantic exploration - Dbrec[Passant,2010] - HyProximity[Stankovic et al,2012] - Page rank[Nguyen et al,2015] - VSM[Khrouf et al,2013] • Path based: - Spreading activation[Hajra et al,2014] - Random walk[Cantador et al,2011] - Vertex discovery[Strobin et al,2014] • Serendipitous Rec. • Explanations • Domain independent • Hierarchical data Machine Learning (ML) • Supervised - Classification - kNN[Ristoski et al,2013] - decision trees[Ostuni et al,2013] - RF[Musto et al,2014] - SVM[Kushwaha et al,2014] - Naïve bayes[Schmachtenberg et al,2014] - Bayesian classifier [Rabello Lopes et al,2014] • Unsupervised - Clustering - k-means[Manoj Kumar et al,2015] - Fuzzy-C means, SOM, PCA[Ostuni etal,2014] • Many algorithms already developed. • Large datasets. • Improve results with experience
  • 11.
    Linked Data basedRecommender Systems 11 SLR: 2. Current gaps of LD based RS Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Topic Issues Datasets • Local copy of LD datasets • Restricted access Manual Operations • Curated datasets – manual review • Selection of concepts for a domain Algorithms • Graph-based: - High cost for exploiting semantic features • ML: - No intrinsic semantic structure of the LD - Ratings and user’s profile information – cold start problem
  • 12.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 13.
    Linked Data basedRecommender Systems • How to recommend resources considering the knowledge available on the web of data, analyzing their implicit relationships? 13 Research Question LD cloud Preprocessing Recommender Algorithm Ranking Input data Recommended resources Background data Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 14.
    Linked Data basedRecommender Systems 1. Recommendation -> semantic relationships from LD 2. Layered framework -> execute and test algorithms to create novel RS. 3. Graph-based algorithms in RS: more reliable -> intrinsic structure of the LD 4. ML algorithms in RS -> do not require the user’s profile information 14 Hypothesis Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 15.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 16.
    Linked Data basedRecommender Systems • A framework for executing and analyzing recommendation algorithms based on LD: AlLied • A selection of algorithms for RS -> knowledge of LD • Implementations: • Graph-based • ML 16 Approach Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 17.
    Linked Data basedRecommender Systems 17 Conceptual architectures Recommender System Management User interface and applications Common Layers Knowledge Base Management Bottom Layer: structured data with user defined vocabularies Middle Layer: semantic web core Top Layer: user interface for applications Semantic Web Stack: (Horrocks et. Al, 2005) Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 18.
    Linked Data basedRecommender Systems Conceptual architectures Recommender System Management User interface and applications Common Layers Knowledge Base Management Conceptual architecture for applications on the web of data: (Heitman et. Al, 2012) RDF store Graph access Data homogenization service Data discovery service Graph query language Graph-based navigation interface Application logic Structured data authoring interface Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 18
  • 19.
    Linked Data basedRecommender Systems 19 AlLied Framework Steps of the recommendation process Resource generation Rec1 Rec2 Recn … Results grouping Rank1 Rank2 … Results ranking Rankn Presentation Recommender System Management User interface and applications AlLied framework Conceptual architectures Knowledge Base Knowledge Base Management Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Candidate Resource generation ranking Results grouping Results presentation Preprocessing
  • 20.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 21.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 21
  • 22.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset DBpedia Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 22 Preprocessing
  • 23.
    Linked Data basedRecommender Systems • Dbpedia: • Hierarchical schemas: • Wikipedia categories (SKOS) • Traversal relationships: • Direct and indirect relationships 23 Graph-based: knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 24.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 24 Candidate Resource generation Preprocessing
  • 25.
    Linked Data basedRecommender Systems • Traversal Generator: • Direct and indirect links - properties • Hierarchical Generator: • Distance in the category tree Graph-based: generation Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 25 The dark knight Man of steel American superhero films Batman film Batman begins dbpedia-owl:director Christopher Nolan dbpedia-owl:director Superhero films Superhero comedy films Megamind Initial Resource The mask Warner bros dbpedia-owl:distributor dbpedia-owl:distributor dbpedia-owl:distributor 1 2 3 3
  • 26.
    Linked Data basedRecommender Systems Hierarchical Generator Traversal Generator 26 Graph-based: generation: ReDyAl algorithm Initial Resource Traversal Generation CR>= MinRes LinksIR >= MinLinks Ranking Create category graph CR>= MinRes Update category graph CurrLevel >= MaxDist Y Y Y Y Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Hierarchical Generation currLevel++
  • 27.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 27 Candidate Resource generation ranking Preprocessing
  • 28.
    Linked Data basedRecommender Systems • HyProximity: [Stankovic., et al, 2011] ℎ𝑦𝑃 𝑐%, 𝑐' = 𝑝 𝑐%, 𝑐' 𝑑 𝑐%, 𝑐' • Hierarchical measures: categories 𝑝 𝑐%, 𝑐' = 1 − 𝑙𝑜𝑔(ℎ𝑦𝑝𝑜 𝐶 + 1) 𝑙𝑜𝑔 𝐶 • Traversal measures: properties 𝑝 𝑐%, 𝑐' = −𝑙𝑜𝑔 𝑛 𝑀 • Linked Data Semantic Distance: [Passant, 2010] • Traversal distance: input/output direct and indirect links 𝐿𝐷𝑆𝐷 𝑐%, 𝑐' = 1 1 + 𝐶𝑑9:; + 𝐶𝑑<= +𝐶𝑖9:; +𝐶𝑖<= • Hybrid: • Traversal + hierarchical 28 Graph-based: ranking Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art distance weighting function
  • 29.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 29 Candidate Resource generation ranking Results grouping Preprocessing
  • 30.
    Linked Data basedRecommender Systems • Meaningful broader category • Less common ancestor 30 Graph-based: results categorizer Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Batman begins American Superhero films Batman Films Man of steel Christopher Nolan Superhero films Superhero comedy films Megamind English directors The dark knight Neo noir films The mask Film noir Crime films Films genres Warner bros American film studios Entertainment
  • 31.
    Linked Data basedRecommender Systems Graph-based Implementation Hierarchical Traversal Dynamic ReDyAl HyProximity LDSD Hybrid Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Remote Dataset (Dbpedia) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 31 Candidate Resource generation ranking Results grouping Results presentation Preprocessing
  • 32.
    Linked Data basedRecommender Systems • User study: • Domain: films • 109 students (PoliTo and Unicauca) • 45 queries (top films of IMDb) • Executed AlLied algorithms • Test List: merging top 10 films for each query and each algorithm. • Only top 20 results of the merged list • Measures: • RMSE: the square root of the mean of the square of all of the error. • Novelty: relevant recommended resources not known by the total number of resources 32 Graph-based: experimental setup Benchmarking website: http://natasha.polito.it/RSEvaluation/ Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 33.
    Linked Data basedRecommender Systems Graph-based: experimentation Bad performers High novelty Sweet spot Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art Traversal + LDSD
  • 34.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 35.
    Linked Data basedRecommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 35
  • 36.
    Linked Data basedRecommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset LODMatrix Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 36 Preprocessing Steps of the recommendation process
  • 37.
    Linked Data basedRecommender Systems • LODMatrix • Creation of a dataset tailored for ML T Need to know the application domain -> obtain only relevant data from that domain • Films from DBpedia • Data mining: quality of results can only be good as the input data [Paulheim., et al, 2012] • FDQ-KDT framework: [Corrales et al, 2015] • Address poor quality data in knowledge discover tasks. 37 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 38.
    Linked Data basedRecommender Systems • LODMatrix 38 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art FDQ-KDT framework: [Corrales et al, 2015]
  • 39.
    Linked Data basedRecommender Systems • LODMatrix: • Attribute Selection: • Common features in state of the art RS: Discovery hub, LinkedMDB, SemMovieRec, Cinemappy, etc. • Matrix Generation: • All possible combinations of the attributes for each film. • 101000 films approx. (about 3 million instances) 39 ML: Knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 40.
    Linked Data basedRecommender Systems • LODMatrix 40 ML: Knowledge base Data extraction • Attribute selection • Matrix generation Data quality assessment • Outliers detection • LOF • Tuckey’s method Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art FDQ-KDT framework: [Corrales et al, 2015]
  • 41.
    Linked Data basedRecommender Systems • LODMatrix: Data quality assessment: • DBpedia -> lot of missing and wrong data (outliers). • Local Outlier Factor: • local density given by the kNN • Tuckey’s method: • 1.5 Interquartile range • LODMatrix was depurated -> fixing data containing outliers • Automatic: Free web services -> fix erroneous data: • The Movie DB • OMDb • Manual fixing : tedious task. 41 ML: Knowledge base Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 42.
    Linked Data basedRecommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 42 Candidate Resource generation Preprocessing
  • 43.
    Linked Data basedRecommender Systems ML: Generation Extract films in the same cluster as the query film Classification Model IR Query film Is it a new film? LODMatrix with cluster data Y Inferring cluster Candidate Resources N Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 43 • Generation phase: • Extracts films belonging to the same cluster as the IR -> reduces the search space • For new films uses the classification algorithm
  • 44.
    Linked Data basedRecommender Systems ML: Generation LODMatrix Clustering Classification Classification Model Training Data Cluster model Add clusters to lodmatrix Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 44 • Training phase: • Clustering: • Discover a set of clusters –> similar features • Classification: • New films (not in the LODMatrix) • Predicts the cluster based on attributes and a trained model.
  • 45.
    Linked Data basedRecommender Systems • Clustering: • K-means: most popular algorithm in RS based on LD: R Easy to use, fast algorithm and good quality clusters T k value selection • Selection of k value empirically: 2 – 100 clusters • Distance • Distribution • Density • K = 55 -> best values ML: Generation Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 45
  • 46.
    Linked Data basedRecommender Systems • Classification: Algorithms selected: most commonly used for RS in the state of the art. ML: Generation 13,22% 16,83% 75,67% 75,69% 0% 10% 20% 30% 40% 50% 60% 70% 80% kNN Naive Bayes Decision Tree Random Forest Classification Error (%) Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 46
  • 47.
    Linked Data basedRecommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 47 Candidate Resource generation ranking Preprocessing
  • 48.
    Linked Data basedRecommender Systems • Mixed Euclidean: R heterogeneous distance measure • Bregman Divergences: • Distance consistent with the NDCG [Acharyya et al,2012] • Itakura Saito, logarithmic loss, logistic loss, squared Euclidean and squared loss. R Easy to implement R Heterogeneous data R Execution time 48 ML: Ranking Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 49.
    Linked Data basedRecommender Systems ML implementation Mixed Euclidean Results Categorizer Visual Interface REST Interface Tree Graph Log Query Controller Local Dataset (LODMatrix) Presentation Grouping Generation Ranking Knowledge Base Management Recommender System Management Clustering K-Means Classification kNN Bregman Distances Steps of the recommendation process Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art 49 Candidate Resource generation ranking Results grouping Results presentation Preprocessing
  • 50.
    Linked Data basedRecommender Systems • User study: • Used the lists generated in the user study for the graph based algorithms 50 ML: Experimental Setup AlLied • Measures: • Precision: fraction of retrieved resources that are relevant • Recall: fraction of relevant instances that are retrieved • F-Measure: harmonic mean between precision and recall Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art • Gold standard: • IMDb gold standard for films. • Top 10 films from IMDb for each query film
  • 51.
    Linked Data basedRecommender Systems 51 ML: User Study 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% Mixed Euclidean Generalized Divergence Itakura Saito Logarithmic Loss Logistic Loss Squared Euclidean Squared Loss Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 52.
    Linked Data basedRecommender Systems 52 ML: Gold standard study 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% Mixed Euclidean Generalized Divergence Itakura Saito Logarithmic Loss Squared Euclidean Squared Loss Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 53.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 54.
    Linked Data basedRecommender Systems 54 Comparative evaluation: User Study 43,2% 20,0% 35,7% 22,0% 18,7% 9,9% 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% ReDyAl - Hybrid Ranker ML - Mixed Euclidean Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 55.
    Linked Data basedRecommender Systems 55 Comparative evaluation: Gold standard 34,2% 24,2% 28,5% 20,1% 15,5% 11,0% 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% 35,0% 40,0% 45,0% 50,0% ReDyAl - Hybrid Ranker ML - Mixed Euclidean Precision Recall F-Measure Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 56.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 57.
    Linked Data basedRecommender Systems • First SLR in LD-based RS • Allied: framework for testing and executing LD based RS • Implementation: graph-based and ML algorithms • ReDyAl: a dynamic algorithm for LD based RS • User and gold standard studies to assess accuracy • 14 research papers published during the PhD. • Directly related with the research topic: • 4 JCR journal papers • 2 conference papers (LNCS -Springer) • Others: 8 papers Contributions 57 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 58.
    Linked Data basedRecommender Systems 1. Motivation 2. State of the art 3. Problem definition 4. AlLied Framework I. Graph-based II. Machine learning 5. Comparative evaluation 6. Contributions 7. Conclusions and future works Outline
  • 59.
    Linked Data basedRecommender Systems • AlLied • Dividing the recommendation process in meaningful phases help to: R Test and use different types of algorithms for each phase R Modular, extensible and reusable • Use of Semantic web standards R New RS with different configurations of algorithms R Reproducibility of results • ReDyAl: • Accurate and Novel recommendations Conclusions 59 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 60.
    Linked Data basedRecommender Systems • ML and graph based algorithms for LD-based RS: • Graph-based implementation: • Advantage of the intrinsic relationships among data – graph structure of LD: R Better accuracy T Cannot deal with huge datasets • ML implementation: R Can recommend resources without user profile information R Deal with large amounts of data. R Classification -> new resources T Limited application domain T Datasets need to be adapted for ML • ML can be combined with graph-based algorithms to improve results. Conclusions 60 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 61.
    Linked Data basedRecommender Systems • TellMeFirst: • Integration of ReDyAl and multisource • “Semantic Annotation and Classification in Practice” published on “IEEE ITPro Magazine” • Mobile application for films recommendation: • “ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data” presented on CBRecSys 2016 • Touristic recommender with Smart Spaces: • Associate touristic attraction with concepts of LD • “Linked Data-Driven Smart Spaces” presented on ruSMART 2014 • Multimodal Search of Business Processes: • Integration of the hierarchical generator -> hierarchical index for a BP repository. • “Improving Business Process Retrieval Using Categorization and Multimodal Search” published on “Knowledge-based Systems” journal Use cases 61 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 62.
    Linked Data basedRecommender Systems • More types of algorithms for the AlLied framework • Explanations of recommendations based on the knowledge of LD • Evaluation of accuracy and performance for algorithms. • Determine best algorithms for each component of the AlLied framework. • Personalized recommendations with LD Future works 62 Motivation Problem AlLied Evaluation ConclusionsContributionsState of the Art
  • 63.
    Linked Data basedRecommender Systems 63 Acknowledgements • COLCIENCIAS • PhD grant • Politecnico di Torino • SoftEng group • Universidad del Cauca • VRI • Telematics Engineering Group
  • 64.
    Thanks!!! Recommender Systems basedon Linked Data Cristhian Figueroa cristhian.figueroa@polito.it cfigmart@unicauca.edu.co PhD Maurizio Morisio PhD Juan Carlos Corrales Politecnico di Torino Universidad del Cauca