Semantics-aware Recommender Systems Exploiting Linked Open Data and Graph-based Features

@cataldomusto
Semantics-aware Recommender Systems
Exploiting Linked Open Data
and Graph-based Features
CATALDO MUSTO, PASQUALE LOPS, MARCO DE GEMMIS, GIOVANNI SEMERARO
UNIVERSITÀ DEGLI STUDI DI BARI ‘ALDO MORO’ - ITALY
World Wide Web Conference 2018
Journal Track
Lyon, France
April 25, 2018
cataldo.musto@uniba.it

What are we going to talk about?
Cataldo Musto, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro
Semantics-aware Recommender Systems Exploiting Linked Open Data and Graph-based Features. WebConf 2018 – Journal Track. Lyon, France. April 25, 2018

Technology able to push
relevant items (movies, news,
books, etc.) to the user
according to her preferences.
Recommender Systems

Recommender Systems
Largely adopted in industry

Recommendation Paradigms
Collaborative
Filtering
Content-based
RecSys

Collaborative
Filtering
Exploits the preferences of the
community to generate
recommendations.
Insight: to suggest items liked
by users similar to the target
one

Content-based
RecSys
Exploit descriptive features
of the items (e.g. genre of a
book, director of a movie) to
generate recommendations.
Insight: to suggest items
similar to those the user
already liked

Hybrid RecSys
+
Combine different recommendation paradigms to provide recommendations.
Advantage: to merge the strength of each paradigm in a unique representation

Hybrid Recommender Systems
• Several hybridization strategies presented in literature (*)
• «Feature Combination» strategy
• Learns a representation of the item that combines heterogeneous
groups of features
• Each representation can be used as a positive/negative example to
tackle the recommendation problem as a text classification one
(*) Burke, Robin. "Hybrid recommender systems:
Survey and experiments." User modeling and
user-adapted interaction 12.4 (2002): 331-370.

• Several hybridization strategies presented in literature
groups of features
…..

groups of features
…..
Group 1 Group 2 Group 3 Group N

• Learns a representation of the item that combines heterogeneous groups of
features
• Groups of basic features
• Popularity-based features
• Collaborative features
• Content-based features
• … etc.

Basic features: popularity-based
•Features based on the popularity of the item
•Examples
• #users who have voted the item
• #user who have positively voted the item
• #ratio of positive ratings
• …etc
…..
Popularity-based Group 2 Group 3 Group N
10 7 0.7 1

Basic features: collaborative
•Features based on the behavior of the users
•Based on the user-item rating matrix
• A column vector is extracted from the matrix and it is
encoded in the representation of the item
…..
Popularity-based Collaborative Group 3 Group N
10 7 0.7 1 1 1 1 1 10 0
i1 i2 … iN
u1 0 1 1 1
u2 1 1 0 1
.. … … … …
uN 0 1 0 0

Basic features: content-based
•Features based on the textual description of the item
•Based on natural language processing techniques
• Textual description is processed
• Typically stopwords are removed and lemmatization is performed
• Sometimes more sophisticated techniques (e.g. semantic analysis, entity linking, etc.) are
implemented
• Output: a set of tokens describing the item, modeled as a vocabulary
…..
Popularity-based Collaborative Content-based Group N
10 7 0.7 1 1 1 1 1 10 0

Basic features: content-based
…..
10 7 0.7 1 1 1 1 1 10 0
Feature Score
science-fiction 1
horror 0
programmer 1
lawyer 0
…. …
dystopian 1
1 10 1

Research Question
…..
10 7 0.7 1 1 1 1 1 10 0
Is there any other information source that can
be exploited to enrich item representation in a
hybrid recommendation framework?
?
1 10 1

Linked Open Data cloud
This is the Linked Open Data cloud

It is a (huge) set of interconnected
semantic datasets
Each bubble is a dataset!

It is a (huge) set of interconnected
semantic datasets
Each bubble is a dataset!
How many datasets we have?
149 billions triples
and 9,960 datasets
(source: http://stats.lod2.eu)

The core of the
is DBpedia (http://www.dbpedia.org)
RDF mapping
of Wikipedia

DBpedia
Wikipedia
Unstructured Content

DBpedia
Wikipedia
Unstructured Content
DBpedia
Structured Data (in RDF)

DBpedia
Very fine-grained
and interesting
features!

Research Question (again)
How can we use Linked Open Data features for Recommender Systems?

Extended features: LOD-based
Such features can be
extracted from the
LOD cloud and can be
encoded as a novel
vocabulary of
features that
enriches the item
representation

Extended features: LOD-based
…..
Popularity-based Collaborative Content-based LOD-based
features
10 7 0.7 1 1 1 1 1 10 0 1 10 1
Feature Score
Don Davis 1
Ennio Morricone 0
Dystopian Movie 1
Brad Pitt 0
…. …
Films about Rebellions 1
1 0 1

Extended features: graph-based
Graph-based features describing
the item can be inferred by
mining the structure of the
tripartite graph
Average Neighbor degree
Degree Centrality
Node redundancy
Clustering coefficient

Extended features: graph-based
…..
Popularity-based Collaborative Content-based Graph-based
10 7 0.7 1 1 1 1 1 10 0 1 10 1
Feature Score
Average Neigh. Degree 1.7
Degree Centrality 2
Clustering Coefficient 0
Node Redundancy 0.3
1.7 2 0

Hybrid RecSys & LOD-based features
Research Question: what is the impact of such
features on the overall performance of a hybrid
recommendation framework?

Research Question: what is the impact of such
features on the overall performance of a hybrid
recommendation framework?
Insight: to evaluate the performance of the
recommender system on varying of the different
groups of features
Hybrid RecSys & LOD-based features

Datasets
MovieLens 1M (ML1M)
6,040 users
3,883 movies
1,000,209 ratings
57.51% positive ratings
165.59 ratings/user (avg.)
269.88 ratings/item (avg.)
99.4% sparsity

Datasets
DBbook
6,181 users
6,733 movies
72,732 ratings
45.86% positive ratings
11.71 ratings/user (avg.)
10.74 ratings/item (avg.)
99.8% sparsity

Experimental Protocol: item representation
Popularity-based Collaborative Content-based
10 7 0.7 1 1 1 1 0 0 1 10 1
We first model basic features

1
10 7 0.7 1 1 1 1 0 0 1 10 1
Then we introduce extended features based on the LOD cloud
LOD-based
1 0 2
Graph-based
1 2

1
10 7 0.7 1 1 1 1 0 0 1 10 1
We use them to feed a hybrid classification framework
LOD-based
1 0
Graph-based
1 22

Experimental Protocol: experiments
Experiment 1: impact of basic features on the performance
10 7 0.7 1 1 1 1 0 0 1 10 1

Experiment 2: impact of extended features on the performance
11 0 21 2

Experiment 3: impact of both groups of features on the performance
11 0 21 210 7 0.7 1 1 1 1 0 0 1 10 1

Experiment 4: comparison to state-of-the-art algorithms
11 0 21 210 7 0.7 1 1 1 1 0 0 1 10 1

Experiment 1
Collaborative and
popularity-based
features got the
best results
53,38
56,156,18
54,21
49,13
55,32
56,35 56,27
50,51
55,67
51,87
55,49
52,46
55,83
48
49
50
51
52
53
54
55
56
57
MovieLens Dbbook
Popularity (P) Collaborative (C) Content-based (T) P+C P+T C+T P+C+T
Results of the Experiments

Experiment 2
LOD-based and
graph-based
features alone
did not overcome
the basic
features
56,35 56,27
50,65
55,44
50,54
54,67
48
49
50
51
52
53
54
55
56
57
MovieLens Dbbook
Popularity+Collaborative LOD-based Graph-based

Experiment 3
The injection of
LOD-based
features improves
the predictive
accuracy
56,35
56,27
56,42
56,59
56,21
56,07
56,78
56,67
55,5
55,7
55,9
56,1
56,3
56,5
56,7
56,9
MovieLens Dbbook
Popularity+Collaborative (Baseline) Baseline+LOD
Baseline+Graph Baseline+LOD+Graph

Experiment 4
Our framework
overcomes all the
baselines
56,78 56,67
42,7
51,93
43,2
51,11
52,18
52,9
52,15
53,04
53,97
55,02
54
55,4
41
43
45
47
49
51
53
55
57
MovieLens Dbbook
LOD-based RecSys U2U-KNN I2I-KNN BPRMF
BPRMF+LOD PPR PPR+LOD
U2U-KNN: User-to-User
Collaborative Filtering
I2I-KNN: Item-to-Item
Collaborative Filtering
BPRMF: Bayesian Personalized
Ranking Matrix Factorization
PPR: Personalized PageRank

LOD-based RecSys
Take Home Messages
Future Trends
• Linked Open Data && (Graph Embeddings || Word Embeddings)
• Linked Open Data && Different Metrics (Serendipity, Novelty, etc.)
1. Linked Open Data represent a huge data silos, which is freely available
2. They can feed machine learning models with new and relevant features
3. They improve the accuracy of recommender systems

Want to read more?
cataldo.musto@uniba.it
@cataldomusto
Semantics-aware Recommender Systems exploiting
Linked Open Data and graph-based features
C Musto, P Lops, M de Gemmis, G Semeraro
Knowledge-Based Systems 136, 1-14

Semantics-aware Recommender Systems Exploiting Linked Open Data and Graph-based Features

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