a linked data recommender system using a neighborhood-based graph kernel

EC-Web 2014 –The 15th International Conference on Electronic Commerce and Web Technologies September 1-4, 2014 Munich, Germany

A Linked Data Recommender System using a Neighborhood-based Graph Kernel

Vito Claudio Ostuni, Tommaso Di Noia, Roberto Mirizzi*, Eugenio Di Sciascio

{vitoclaudio.ostuni, tommaso.dinoia, eugenio.disciascio}@poliba.it, [email protected]

Polytechnic University of Bari - Bari (ITALY) Yahoo! Sunnyvale, CA (US) (*)


Outline

Introduction and motivation

The proposed approach

Experimental Evaluation

Contributions and Conclusion


Recommender Systems

Help users in dealing with Information/Choice Overload


Model-based approach: Feature vector about item content description Learn a predictive user model from past user preferences

A definition CB-RSs try to recommend items similar* to those a given user has liked in the past [P. Lops, M. de Gemmis, G. Semeraro. Content-based Recommender Systems: State of the Art and Trends. Recommender Systems Handbook.]

Content-based RSs

drama

action Heat

Argo The Godfather

Righteous Kill

(*) similar from a content-based perspective


Motivation

Traditional Content-based Recommender Systems:

• base on keyword/attribute -based item representations

• rely on the quality of the content-analyzer to extract expressive item features

• lack of knowledge about the items


Motivation

Traditional Content-based Recommender Systems:

• base on keyword/attribute -based item representations

• rely on the quality of the content-analyzer to extract expressive item features

• lack of knowledge about the items

• use Linked Open Data to obtain knowledge about items and richer item representations


Linked Open Data

• Initiative for publishing and connecting data on the Web using Semantic Web technologies;

• >30 billion of RDF triples from hundreds of data sources;

• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]

http://www.w3.org/2008/Talks/0617-lod-tbl/(3)








Graph-based Item Representation

The Godfather

Mafia_films

Gangster_films

American Gangster

Films_about_organized_crime_in_the_United_States

Best_Picture_Academy_Award_winners

Best_Thriller_Empire_Award_winners

Films_shot_in_New_York_City

subject

subject subject

subject

subject

subject

subject



The Godfather

Mafia_films Films_about_organized_crime

Gangster_films

American Gangster


Films_about_organized_crime_by_country



Awards_for_best_film


subject

subject subject

broader

broader

broader

broader

broader

subject

subject

subject

subject



The Godfather


Gangster_films

American Gangster







subject

subject subject

broader

broader

broader

broader

broader

broader

subject

subject

subject

subject



The Godfather


Gangster_films

American Gangster







subject

subject subject

broader

broader

broader

broader

broader

broader

subject

subject

subject

subject

Exploit entities descriptions


h-hop Item Neighborhood Graph

The Godfather


Gangster_films

Best_Picture_Academy_Award_winners Awards_for_best_film


subject

subject subject

broader

broader

broader


Challenges

• learn the user model starting from semantic graph-based item representations (h-hop Item Neighborhood Graph)

• exploit the knowledge associated to the items


Proposed Approach

• define an appropriate kernel on graph-based item representations

• use kernel methods for learning the user model

• learn the user model starting from semantic graph-based item representations (h-hop Item Neighborhood Graph)

• exploit the knowledge associated to the items


Kernel Methods

Work by embedding data in a vector space and looking for linear patterns in such space

𝑥 → 𝜙(𝑥)

[Kernel Methods for General Pattern Analysis. Nello Cristianini . http://www.kernel-methods.net/tutorials/KMtalk.pdf]

𝜙(𝑥)

𝜙 𝑥 Input space Feature space

We can work in the new space F by specifying an inner product function between points in it

𝑘 𝑥𝑖, 𝑥𝑗 = < 𝜙(𝑥𝑖), 𝜙(𝑥𝑗)>


h-hop Item Neighborhood Graph Kernel

Explicit computation of the feature map

entity importance in the item neigh. graph


h-hop Item Neighborhood Graph Kernel

Explicit computation of the feature map

# edges involving em at l hop from i

frequency of the entity in the

item neigh. graph

proportional factor taking into account at which hop the entity appears


Weights computation example

i

e1 e2

p3

p2

e4

e5

p3 p3

h=2


Weights computation example

i

e1 e2

p3

p2

e4

e5

p3 p3

h=2

Informative entity about the item even if not directly related to it


Experimental Settings

Trained a SVM Regression model for each user Accuracy Evaluation: Precision, Recall,MRR (Rated Test Items protocol) Novelty Evaluation: Entropy-based Novelty (All Items protocol) [the lower the better]


Dataset

Subset of Movielens mapped to DBpedia 6,040 users 3,148 movies

Mappings of various recsys datasets to DBpedia http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/

Three different train/test splits 20/80, 40/60, 80/20

http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/




Kernel calibration – impact of alpha params. (i)

0,5

0,55

0,6

0,65

0,7

0,75

0,25 1 2 5 10 20

Prec@10 [20/80]

Prec@10 [40/60]

Prec@10 [80/20]


Kernel calibration – impact of alpha params. (ii)

0

0,2

0,4

0,6

0,8

1

1,2

0,25 1 2 5 10 20

EBN@10 [20/80]

EBN@10 [40/60]

EBN@10 [80/20]


Comparative approaches

•NB: 1-hop item neigh. + Naive Bayes classifier

•VSM: 1-hop item neigh. Vector Space Model (tf-idf) + SVM regr

•WK: 2-hop item neigh. Walk-based kernel + SVM regr


Comparison with other approaches (i)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

Prec@10 [20/80] Prec@10 [40/60] Prec@10 [80/20]

NK-bestPrec

NK-bestEntr

NB

VSM

WK


Comparison with other approaches (ii)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

EBN@10 [20/80] EBN@10 [40/60] EBN@10 [80/20]

NK-bestPrec

NK-bestEntr

NB

VSM

WK


Contributions

A linked data RS based on kernel methods

Exploitation of semantic graph-based item descriptions from the Web of Data

Effective Item Neighborhood Graph Kernel

Combination of kernel methods and LOD based item descriptions for model-based Content-based recommendations

Future Work:

Evaluation of further kernel functions on graphs

Evaluation of different kernel methods


Q & A

a linked data recommender system using a neighborhood-based graph kernel

Data & Analytics