semantics-aware graph-based recommender systems exploiting linked open data

Semantics-aware Graph-based Recommender Systems exploiting

Linked Open DataCataldo Musto, Pasquale Lops, Pierpaolo Basile,

Marco de Gemmis Giovanni Semeraro (Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)

UMAP 2016 24th Conference on User Modeling,

Adaptation and Personalization Halifax (Canada)

July 15, 2016

Linked Open Data

Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Linked Open Data

Methodology to publish, share and link structured data on the Web

Definition

Linked Open DataCornerstones

2.Use of RDF to publish data on the Web

Re-Use of existing properties to express an agreed semantics and connect data sources

Linked Open Data (cloud)What is it?

A (large) set of interconnected semantic datasetsCataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Linked Open Data (cloud)What kind of datasets?

Each bubble is a dataset!Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Linked Open Data (cloud)How many data?

9960 datasets and 149 billions triplessource: http://stats.lod2.eu

today!

Linked Open Data (cloud)

DBpedia is the structured RDF mapping of Wikipedia

http://dbpedia.org

It is the core of the LOD cloud.

DBpedia

Linked Open Data (cloud)Example: unstructured content from Wikipedia

example (Wikipedia page)

Linked Open Data (cloud)How are these data represented?

The Matrix

Don Davis

http://dbpedia.org/resource/Category:Films_shot_in_Australia

Films shot in Australia

dcterms:subject

dbpedia-owl:musicComposer

http://dbpedia.org/resource/Don_Davis_(composer)

dcterms:subject

ordcterms:subject

dcterms:subject

Dystopian Films136American Action Thriller Films

Cyberpunk Films The Wachowskis

http://dbpedia.org/resource/The_Wachowskis

http://dbpedia.org/resource/Dystopian_FIlms

http://dbpedia.org/resource/Cyberpunk_Films

http://dbpedia.org/resource/American_Action_Thriller_FIlms

http://dbpedia.org/resource/Films_About_Rebellions

Films about Rebellions

Several interesting (non-trivial) features come into play!Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Linked Open Data (cloud)How are these data represented?

The Matrix

Don Davis

http://dbpedia.org/resource/Category:Films_shot_in_Australia

Films shot in Australia

dcterms:subject

dbpedia-owl:musicComposer

http://dbpedia.org/resource/Don_Davis_(composer)

dcterms:subject

ordcterms:subject

dcterms:subject

Dystopian Films136American Action Thriller Films

Cyberpunk Films The Wachowskis

http://dbpedia.org/resource/The_Wachowskis

http://dbpedia.org/resource/Dystopian_FIlms

http://dbpedia.org/resource/Cyberpunk_Films

http://dbpedia.org/resource/American_Action_Thriller_FIlms

Several interesting (non-trivial) features come into play!Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Research Questions

(1) Can we use Linked Open Data for Recommender Systems?

Research Questions

(2) Is it possible to automatically select the most promising properties among those available in the LOD cloud?

MethodologyGraph-based Data Model - original representation

Original Graph-based data model

Users and Items are connected according to users’ preferences

MethodologyGraph-based Data Model - DBpedia Mapping

If we are able to map the items in the dataset with

the entities in the

LOD cloud, our representation can be extended with new data points

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

MethodologyGraph-based Data Model - LOD-boosted representation

1999 films

http://dbpedia.org/resource/1999_films

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

Methodologyexample - LOD-boosted representation

1999 films

dcterms:subject

Many new information can be injected in the

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

1999 films

dcterms:subject

How to get recommendations?

Graph-based Recommendation Algorithm

Insight: - Calculate PageRank score for each item node. - Sort PageRank scores in a descending order. - Select top-k recommendations

PageRank with Priors

. T. H. Haveliwala. Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. IEEE Trans. Knowl. Data Eng., 15(4):784–796, 2003.

Reference

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

1999 films

dcterms:subject

Is it possibile to automatically select the most promising

properties?

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

1999 films

dcterms:subject

dbprop:director

Quentin Tarantino

dbprop:director

1999 films

dcterms:subject

dcterms:subjectXX

Experiments

Research QuestionsDo graph-based recommender systems benefit of the introduction of LOD-based features?

Do graph-based recommender systems exploiting LOD benefit of the adoption of feature selection techniques?

Research Questions3.

2/2Is there any correlation between the choice of the FS technique and the behavior of the algorithm? (e.g., better diversity or better F1) ?

How does our methodology perform with respect to state-of-the-art algorithms?

Experimental EvaluationDescription of the dataset

MovieLens 100k983 users1,682 movies100,000 ratings55.17% positive ratings84.43 ratings/user (avg.)48.48 ratings/item (avg.)93.7% sparsity

Experimental EvaluationDescription of the dataset

DBbook dataset6,181 users6,733 movies72,372 ratings45,85% positive ratings11.70 ratings/user (avg.)10.74 ratings/item (avg.)99.8% sparsity

Experimental EvaluationGraph Representations :: Recap

GBasic Graph with collaborative data points

Experimental Evaluation

GLOD Graph extended with all the properties gathered from the LOD cloud

Graph Representations :: Recap

Experimental Evaluation

GLOD+FS Graph encoding only the most relevant properties selected by a feature selection technique FS

Graph Representations :: Recap

Experimental EvaluationExperimental Protocol

Algorithm PageRank with Priors

Data Split 5-fold Cross Validation for MovieLens Train/Test for DBbook

Graph Representation G, GLOD, GLOD+FS

Feature Selection Techniques PageRank, Chi-Square, Information Gain, Gain Ratio, mRMR, PCA, SVM

#Selected Features top-10, top-30, top-50 properties

Evaluation Metrics F1, Intra-List Diversity, Run Time

Experiment 1

Impact of LOD-based features.

53 55 57 59 61

Experiment 1

Impact of LOD-based features :: F1-measure

Improvement only on MovieLens

53 56 59 62 65

MovieLens

DBbook

Run Time (min.)

50 262,5 475 687,5 900

Experiment 1

34Tremendous increase in the run time

Impact of LOD-based features :: Run Time

Run Time (min.)

50 662,5 1275 1887,5 2500

MovieLens

DBbook

Experiment 2

Impact of Feature Selection techniques

Experiment 2

36GLOD (baseline) = 54,24

Impact of Feature Selection :: MovieLens :: F1@5

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

53 53,5 54 54,5 5554,31

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

53 53,5 54 54,5 5554,31

Experiment 2

Tree out of seven techniques (and only with 50 features) overcome the baseline

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

53 53,5 54 54,5 5554,31

Experiment 2

38Typically, the larger the number of features, the better the F1

(best)

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

63,5 63,825 64,15 64,475 64,864,286

Experiment 2

Impact of Feature Selection :: DBbook :: F1@10

GLOD (baseline) = 64,20Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

63,5 63,825 64,15 64,475 64,864,286

Experiment 2

All the techniques overcome the baseline at least once

PageRank

Chi-Square

Gain Ratio

Inf. Gain

103050103050103050103050103050103050103050

63,5 63,825 64,15 64,475 64,864,286

Experiment 2

41On DBbook best results are obtained with 10 features!

(best)

Run Time (min.) - MovieLens

GLOD+PCA

50 262,5 475 687,5 900

Experiment 2

42Significant decrease

Impact of Feature Selection techniques :: Run Time

Run Time (min.) - DBbook

GLOD+IG

50 687,5 1325 1962,5 2600

Experiment 3

Trade-off between F1 and diversity

Experiment 3

Trade-off between F1 and diversity

Can the choice of the feature selection technique endogenously induce an higher diversity (or,

respectively, an higher F1) of the recommendations?

Experiment 3

Trade-off between F1 and diversity :: MovieLens :: F1@5

G_LOD = Baseline

Experiment 3

PCA maximizes F1, at the expense of a little diversity

Experiment 3

Gain Ratio and SVM sacrifice F1, to induce an higher diversity

Experiment 3

PageRank obtains a good compromise between F1 and Diversity

Experiment 3

Similar outcomes on DBbook…but more techniques have a good impact

Experiment 4

Comparison to State of the artBPRMF (Bayesian Personalized Ranking) [+]

U2U-KNN (User to User CF) I2I-KNN (Item to Item CF)

POPULAR (Popularity-based baseline)

[+] S. Rendle, C.Freudenthaler, Z. Gantner, L. Schmidt-Thieme: BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009.

Experiment 4

Comparison to State of the Art :: MovieLens

F1@5 F1@10

I2I-KNN U2U-KNN BPRMF POPULAR PR (G_LOD+PCA)

Experiment 4

F1@5 F1@10

PageRank with Priors boosted with LOD is the best-performing approach

Experiment 4

F1@5 F1@10

Even state-of-the-art approaches based on Matrix Factorization are overcame by our methodology

Experiment 4

F1@5 F1@10

Behavior confirmed on DBbook

Comparison to State of the Art :: DBbook

Conclusions

55Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

Methodolology1. PageRank with Priors as base algorithm2. Mapping of the items with nodes in the Linked

Open Data Cloud 3. Expansion of the data points and injection of new

nodes and edges 4. Use of feature selection to automatically select the

most promising properties

INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA INGRAPH-BASED RECOMMENDER SYSTEMS

Lessons Learned

Evaluation1. PageRank with Priors benefit of the injection of data points

coming from the LOD cloud2. Feature Selection techniques improve the results but need

to be properly tuned, since its usage is not always useful 3. A significant connection between the choice of the feature

selection technique and the maximization of a specific evaluation metric exists

4. PageRank with Priors boosted with LOD significantly overcomes state-of-the-art approaches

INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA INGRAPH-BASED RECOMMENDER SYSTEMS

thank youCataldo Musto

cataldo.musto@uniba.it @cataldomusto

http://www.di.uniba.it/~swap

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