Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation

Combining Distributional Semantics
and Entity Linking for Context-aware
Content-based Recommendation
Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis
(Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)
UMAP 2014
22th Conference on User Modeling,
Adaptation and Personalization
Aalborg (Denmark)
July 8, 2014

Content-based Recommender Systems
Suggest items similar to those the user liked in the past
(I bought Converse should, I’ll continue buying similar sport shoes)
Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and
Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 2

Xuser proﬁle items
Recommendation are
generated by matching the
features stored in the user
proﬁle with those
describing the items to be
recommended.
♥

(Some) Limitations
Poor Semantic Representation Poor Contextual Modeling

?
Lack of Semantics
“I love turkey. It’s my choice for these holidays!

Lack of Contextual Modeling
Ashtead?
Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014
in Aalborg:
brewery recommendations
6

Lack of Contextual Modeling
Many content-based recommendation engines
do nothandle contextual information (e.g. user location)
1370km !
far away :-)

contextual eVSM
a context-aware content-based recommendation
framework based on distributional semantics and
entity linking
Our contribution

Contextual eVSM
Workflow
Semantic
Content Analyzer
Context-aware
Profiler
Recommender
Items
User
Profiles
User Ratings
Contextual
Data
Item
Description
Context-aware
Recommendations

Contextual eVSM
3 main components
Semantic !
Content Analyzer!
Context-aware !
Proﬁler!
Recommender!
Items
User
Proﬁles
User Ratings
Contextual
Data
Item
Description
Context-aware
Recommendations

!
• Input: items to be recommended
(along with their textual description)
• Output: semantic representation
• Novelty: we exploited
• Entity Linking algorithms!
• Distributional Semantics Models
Contextual eVSM
Semantic Content Analyzer

Contextual eVSM
• Entity Linking Algorithms!
• Input: free text.
• items description, in our setting
• Output: identiﬁcation of the most
relevant entities mentioned in the text.
• We adopted:
• tag.me(1)
,
• DBpedia Spotlight(2)
,
• Wikipedia Miner(3)
Semantic Content Analyzer :: Entity Linking
(1) http://tagme.di.unipi.it
(2) http://spotlight.dbpedia.org
(3) http://wikipedia-miner.cms.waikato.ac.nz
12

Contextual eVSM
Semantic Content Analyzer :: Entity Linking::Example
Textual Description
(e.g. Wikipedia abstract)
Processed Text
13

Contextual eVSM
Very transparent and human readable content representation
Tag.me output
14

Contextual eVSM
Tag.me output
non-trivial NLP tasks (stopwords removal, n-grams identification, named entities
recognition and disambiguation) are automatically performed
15
Very transparent and human readable content representation

Contextual eVSM
Tag.me output
Each entity is a reference to a Wikipedia page
http://en.wikipedia.org/wiki/The_Wachowskis
not a simple textual feature!
16

Contextual eVSM
We enriched this entity-based representation !
by exploiting the Wikipedia categories’ tree
17

Contextual eVSM
Semantic Content Analyzer :: Entity Linking::Representation
The ﬁnal
representation
of each item is
obtained by
merging the
entities identiﬁed
in the text with all
the Wikipedia
categories each
entity is linked to.
+Entities Wikipedia CategoriesFeatures =
18

Contextual eVSM
Semantic Content Analyzer :: Entity Linking::Representation
The ﬁnal
representation
of each item is
obtained by
merging the
entities identiﬁed
in the text with all
the Wikipedia
categories each
entity is linked to.
+Entities Wikipedia CategoriesFeatures =
Problem:
Even such a rich, transparent and
human-readable representation
does not handle semantics
19

Contextual eVSM
Semantic Content Analyzer :: Distributional Semantics
“meaning
is its use”
L.Wittgenstein
(Austrian philosopher)
20

Contextual eVSM
Semantic Content Analyzer :: Distributional Semantics (*)
by analyzing large corpora of
textual data it is possible to
infer information about the
usage (about the meaning) of
the terms
Insight
similar meaning
co-occurrence co-occurrence
co-occurrence co-occurrence
(*) Firth, J.R.A synopsis of linguistic theory
1930-1955. In Studies in Linguistic Analysis, pp.
1-32, 1957.
21

Contextual eVSM
Semantic Content Analyzer :: Distributional Semantics::WordSpace
beer
wine
mojito
dog
22
Vector-space
representation is based on
term co-occurences

Contextual eVSM
e1 e2 e3 e4 e5 e6 e7 e8 e9
Keanu Reeves ✔ ✔ ✔ ✔ ✔
Al Pacino ✔ ✔
American Writers ✔ ✔ ✔ ✔
Laurence Fishburne ✔ ✔ ✔ ✔
Our Semantic Content Analyzer learns a vector-space item
representation based on distributional semantics models
23

Contextual eVSM
e1 e2 e3 e4 e5 e6 e7 e8 e9
Al Pacino ✔ ✔
Vector-space Semantic Representation is learnt according to
entities co-occurrences in textual descriptions
24

Contextual eVSM
Unexpected connections between
entities can be learnt in a total
unsupervised way thanks to
Distributional Semantics
25

Contextual eVSM
e1 e2 e3 e4 e5 e6 e7 e8 e9
Al Pacino ✔ ✔
e.g. Keanu Reeves and Al
Pacino both starred in
Drama movies
26

Contextual eVSM
How to exploit
Distributional Semantics !
to represent items
to be recommended?
Question
27

Contextual eVSM
e1 e2 e3 e4 e5 e6 e7 e8 e9
Drama ✔ ✔
semantic representation of the items is obtained by combining
the vector-space representation of the features which
describe them.
28

Contextual eVSM
e1 e2 e3 e4 e5 e6 e7 e8 e9
Al Pacino ✔ ✔
Matrix ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
29

Contextual eVSM
Matrix
Matrix Revolutions
Donnie Darko
Up!
It is possible to
perform similarity
calculations
between items
according to their
semantic
representation
30

!
• Input:
• user preferences (ratings)
• contextual information
• Fixed set of contextual dimensions
(company, mood, task, etc.)
• Fixed set of values (e.g. company=alone,
friends, girlfriend, etc.)
• Output: contextual user proﬁles
• Novelty: we introduced a Context-aware
Proﬁling Strategy based on Distributional
Models
Contextual eVSM
Context-aware Profiler

C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj)
Contextual eVSM
Context-aware User Profiler :: Strategy
Let’s go straight to the formula
32

Contextual eVSM
Let u be the target user
Let ck be a contextual variable (e.g. task, mood, etc.)
Let vj be its value (e.g. task=running, mood=sad, etc.)
33

Contextual eVSM
A context-aware proﬁle can be learnt by combining
two components in a linear fashion
34

Contextual eVSM
a non-contextual representation
of user preferences
a vector space representation of
the context itself
35
A context-aware proﬁle can be learnt by combining
two components in a linear fashion

Contextual eVSM
Context-aware User Profiler :: Strategy :: WRI(u)
WRI(u) = ∑ di*
r(u,i)
MAXi=1
|L|
NON-CONTEXTUAL USER
PREFERENCES
36

Contextual eVSM
WRI(u) = ∑ di*
r(u,i)
MAXi=1
|L|
items the user liked
37

Contextual eVSM
WRI(u) = ∑ di*
r(u,i)
MAXi=1
|L| vector-space representation of the
item built by Semantic Content
Analyzer
38

Contextual eVSM
WRI(u) = ∑ di*
r(u,i)
MAXi=1
|L|
normalized rating
39

Contextual eVSM
Context-aware User Profiler :: Strategy :: context(u,ck,vj)
context(u,ck,vj) = ∑ di*
r(u,i,ck,vj)
MAXi=1
|L(ck,vj)| Vector-space
representation of
the context
40

Contextual eVSM
r(u,i,ck,vj)
MAXi=1
|L(ck,vj)|
items the user liked
in that specific context
41

r(u,i,ck,vj)
Contextual eVSM
MAXi=1
|L(ck,vj)| vector space
representation
of the item
42

Contextual eVSM
r(u,i,ck,vj)
MAXi=1
|L(ck,vj)|
normalized rating
in that specific context
43

Contextual eVSM
Ratio: context is just a factor which can inﬂuence
user’s perception of an item
44

Contextual eVSM
if the user did not express any preference in that
speciﬁc contextual setting, context(u,ck,vj) = 0 !
—> non contextual recommendation
45
X

Contextual eVSM
Otherwise parameter α is exploited to tune a
speciﬁc component of the formula
46

Contextual eVSM
Context-aware User Profiler :: How do we come to this formula?
47

Contextual eVSM
Context-aware User Profiler :: How do we come to this formula?
Insight: it exists a set of terms that is more descriptive of
items relevant in that speciﬁc context
for a romantic dinner, e.g. candlelight, seaview, violin
48
e.g. task = dinner, company=girlfriend

Context is represented on the
ground of the items the user
liked in that speciﬁc contextual setting
Contextual eVSM
Context-aware User Profiler :: Our formula inherits this insight
49
r(u,i,ck,vj)
MAXi=1
|L(ck,vj)|

Contextual eVSM
r(u,i,ck,vj)
MAX
Items are represented on the ground of
the co-occurrences between entities
i=1
|L(ck,vj)|

Contextual eVSM
r(u,i,ck,vj)
MAX
Items are represented on the ground of
the co-occurrences between entities
i=1
|L(ck,vj)|
the resulting representation of
the context is such that a
bigger weight is given to the
entities which typically
occur in the description of
the items relevant in that
speciﬁc context

Contextual eVSM
r(u,i,ck,vj)
MAX
Thanks to Distributional Semantics Models it is possible
to build a vector-space representation of the context
which emphasize the importance of those terms,
since they are more used (—> more important) in that
speciﬁc contextual setting.
i=1
|L(ck,vj)|

Contextual eVSM
Recommendation step
Skyfall
WRI(u)
Austin Powers
Up!
The goal of our
context-aware
proﬁling strategy is to
perturb the
representation of user
preferences and to
provide him with
context-aware
recommendations
53
non-contextual preferences

Contextual eVSM
Recommendation step
Skyfall
C-WRI(u)
Austin Powers
Up!
The goal of our
context-aware
proﬁling strategy is to
perturb the
representation of user
preferences and to
provide him with
context-aware
recommendations
54
contextual preferences
(e.g. company = friends)

Experimental Evaluation
Research Hypothesis
1. Does C-eVSM outperform its
non-contextual counterpart?
2. Does the novel representation
based on entity linking and
distributional semantics
outperform a simple keyword-
based one?
3. How does our model perform with
respect to the current literature?

Description of the dataset
• Movie recommendation!
• Subset of IMDB data
• 202 movies (textual features crawled
from Wikipedia)
• 62 users and 1457 ratings!
• 4 contextual dimensions!
• TIME (weekend, weekday)
• PLACE (theather, home)
• COMPANION (alone, friends, boyfriend,
family)
• MOVIE-RELATED (release week or not)

Design of the Experiment
• Dataset and experimental settings
replicate Adomavicius’ experiment (*)!
• Evaluation over 9 different contextual
settings!
• Home, Friends, Non-release, Weekend,
Weekday, GBFriends, TheatherWeekend
and TheatherFriends
• Metric: F1-Measure
• Experimental protocol: bootstrapping!
• 29/30th of the data as training
• 1/30th as test
• Randomly generated, 500 runs
(*) G.Adomavicius et al. , Incorporating contextual information
in recommender systems using a multi-dimensional
approach.ACM Trans. Inf. Systems, 2005

eVSM configurations
• Non-contextual baseline: eVSM!
• WRI profiling strategy
• WQN profiling strategy
• Context-aware framework: C-
eVSM!
• C-WRI profiling strategy
• C-WQN profiling strategy
• Three values for parameter α!
• 0.2 , 0.5, 0.8
8 configurations
for each run

eVSM configurations
• Non-contextual baseline: eVSM!
• WRI profiling strategy
• WQN profiling strategy
• Context-aware framework: C-
eVSM!
• C-WRI profiling strategy
• C-WQN profiling strategy
• Three values for parameter α!
• 0.2 , 0.5, 0.8
• WQN!
• Alternative profiling strategy (*)
• Models negative user
feedbacks as well
• Combines positive and
negative preferences by
means of a Quantum
Negation (**) Operator
(*) C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Random indexing and  
negative user preferences for enhancing content-based recommender
systems. In EC-Web 2011, volume 85 of LNBIP, pages 270–281. 2011.
(**) D. Widdows. Orthogonal negation in vector spaces for modelling word-
meanings and document retrieval. In ACL, pages 136–143, 2003.

Experiment 1
Comparison of C-eVSM vs eVSM (keyword-based)

Experiment 1
Selection of Results :: HOME segment
WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
45 48,75 52,5 56,25 60
58,8
57,82
54,81
53,62
50,6
48,23
46,62
47,62
contextual eVSM improves the F1 measure

Experiment 1
WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
45 48,75 52,5 56,25 60
58,8
57,82
54,81
53,62
50,6
48,23
46,62
47,62
contextual eVSM improves the F1 measure
paired t-test (p<0.05)
baseline
baseline

Experiment 1
WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
45 48,75 52,5 56,25 60
58,8
57,82
54,81
53,62
50,6
48,23
46,62
47,62
α=0.8 is better than α=0.5

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
42,0 45,3 48,5 51,8 55,0
54,39
50,04
45,93
53,18
50,11
50,54
44,91
49,43
Experiment 1
Selection of Results :: FRIENDS segment
Similar outcomes: C-eVSM outperforms eVSM

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
42,0 45,3 48,5 51,8 55,0
54,39
50,04
45,93
53,18
50,11
50,54
44,91
49,43
Experiment 1
Selection of Results :: FRIENDS segment
α=0.2 does not improve F1-measure

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
42,0 45,8 49,5 53,3 57,0
56,78
52,55
48,67
55,94
52,18
49,05
48,24
48,95
Experiment 1
Selection of Results :: NON-RELEASE segment
C-WQN with α=0.8 is typically the best-performing configuration

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
42,0 45,8 49,5 53,3 57,0
56,78
52,55
48,67
55,94
52,18
49,05
48,24
48,95
Experiment 1
Selection of Results :: NON-RELEASE segment
Outcome: context has just to slightly inﬂuence user preferences

Experiment 1
Outcomes
• Contextual eVSM outperforms eVSM
• 8 segments out of 9
• Little statistical signiﬁcance
• Negation is useful when dataset is well-balanced
• Higher α values lead to a better F1
• Best-performing conﬁgurations are C-WQN-0.8 (4
times), C-WRI-0.8 (1 times), C-WRI-0.5 (3 times)

Experiment 2
Comparison of entity-based vs keyword-based
content representation

Experiment 2
WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
40,0 47,5 55,0 62,5 70,0
61,30
61,96
54,81
57,53
56,75
56,38
46,62
56,13
58,80
57,82
53,37
53,62
50,60
48,23
44,56
47,62 Keywords
Entities
Semantic representation improves F1 in all the configurations

Experiment 2
Gaps are significant in 5 out of 8 configurations
WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
40,0 47,5 55,0 62,5 70,0
61,3
61,96
54,81
57,53
56,75
56,38
46,62
56,13
58,80
57,82
53,37
53,62
50,60
48,23
44,56
47,62 Keywords
Entities

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
40,0 47,5 55,0 62,5 70,0
61,3
61,96
54,81
57,53
56,75
56,38
46,62
56,13
58,80
57,82
53,37
53,62
50,60
48,23
44,56
47,62 Keywords
Entities
Experiment 2
Again, higher α values lead to the best F1-measure scores

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
43,0 48,5 54,0 59,5 65,0
58,37
57,2
52,82
58,25
55,68
56,24
49,19
56,17
54,39
50,04
45,93
53,18
50,11
50,54
44,91
49,43 Keywords
Entities
Experiment 2
73
Selection of Results :: FRIEND segment
+6,42%improvement, gap always significant

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
43,0 48,5 54,0 59,5 65,0
58,37
57,2
52,82
58,25
55,68
56,24
49,19
56,17
54,39
50,04
45,93
53,18
50,11
50,54
44,91
49,43 Keywords
Entities
Experiment 2
74
Selection of Results :: FRIEND segment
Negation+ α Higher values ➝ best configuration

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
43,0 48,5 54,0 59,5 65,0
62,16
57,81
54,72
56,45
58,11
57,21
55,82
56,34
52,64
51,40
46,65
50,71
52,87
53,95
52,79
50,91 Keywords
Entities
Experiment 2
75
Selection of Results :: THEATER segment
Best perfoming segment: +6,49% improvement over keywords

WRI
C-WRI-0.2
C-WRI-0.5
C-WRI-0.8
WQN
C-WQN-0.2
C-WQN-0.5
C-WQN-0.8
43,0 48,5 54,0 59,5 65,0
62,16
57,81
54,72
56,45
58,11
57,21
55,82
56,34
52,64
51,40
46,65
50,71
52,87
53,95
52,79
50,91 Keywords
Entities
Experiment 2
76
Selection of Results :: THEATER segment
C-WQN is the best perfoming conﬁguration: +9,52%

Experiment 2
Outcomes
• Novel semantic representation outperforms the keyword-based one
• 7 segments out of 9
• +4% on average, eanging from +1,34% to +6,49%
• Important gaps in terms of F1-measure
• Entity-based outperforms keywords in 65 segments out of 90 (72%)
• Statistically signiﬁcant gap in 52 out of 90 of the comparisons (58%)
• Negation and higher α values lead to a better F1
• Best-performing conﬁgurations are C-WQN-0.8 (3 times), C-WQN-0.5 (2
times), C-WRI-0.5 (2 times)

Experiment 3
Comparison to context-aware CF algorithm(*)
(*) G.Adomavicius et al. , Incorporating contextual
information in recommender systems using a multi-
dimensional approach.ACM Trans. Inf. Systems, 2005

Home
Friends
Weekend
Theater
Nonrelease
Weekday
GBFriends
Theater-Weekend
Theater-Friends
35,0 43,8 52,5 61,3 70,0
60,7
64,1
48
37,9
43,2
60,8
54,2
48,2
39,19
55,96
54,95
50,72
48,02
57,01
61,16
60,39
58,37
61,96
c-eVSM
CACF
Experiment 3
79Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and
Comparison to context-aware CF algorithm
Contextual eVSM overcomes CACF in 7 segments out of 9
✔

Home
Friends
Weekend
Theater
Nonrelease
Weekday
GBFriends
Theater-Weekend
Theater-Friends
35,0 43,8 52,5 61,3 70,0
60,7
64,1
48
37,9
43,2
60,8
54,2
48,2
39,19
55,96
54,95
50,72
48,02
57,01
61,16
60,39
58,37
61,96
c-eVSM
CACF
Experiment 3
Comparison to context-aware CF algorithm
Gap is statistically signiﬁcant in 5 segments out of 7

Recap

Recap
Contextual eVSM: context-aware recommendation framework
Content Representation based on Distributional Semantics and Entity Linking
Profile Learning based on a perturbation of non-contextual preferences with a
semantic representation of the context!
Experimental session confirmed the effectiveness of the framework as well as of
the novel semantic representation!
Framework overcomes a context-aware collaborative filtering baseline

Future Research

Future Research
Evaluation against different
datasets and stronger baselines;
Exploitation of Linked Data and
Open Knowledge Sources for
content representation;
Evaluation of Novelty, Diversity and
Serendipity of the Recommendations;

questions?
Cataldo Musto, Ph.D
cataldo.musto@uniba.it

Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation

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