Contextual eVSM: a context-aware content-based recommendation framework based on distributional semantics

EC-WEB 2013 - 14th International Conference on Electronic Commerce and WebTechnologies
Prague (Czech Republich) - 28.08.13
Contextual eVSM: a context-aware content-based
recommendation framework based on
distributional semantics
Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis

scenario.
C. Musto, G. Semeraro, P. Lops, M. de Gemmis. - Contextual eVSM: a context-aware content-based recommendation
framework based on distributional semantics - EC WEB 2013 - Prague, Czech Republic - 28.08.2013

Prague, EC-Web conference

dinner time

me and Pasquale like sushi.

what doesTripAdvisor suggest?

Good Price

Good Reviews

....Strasbourg?

612 km far away.

612 km far away.
Too much :-)

we ate pizza, as usual.

why did theTripAdvisor
recommendation engine fail?

it doesn’t take into account
contextual information.
context plays a key role in
several recommendation tasks.

I attended last Sigur Ròs concert
in Rome, so I like them

I attended last Sigur Ròs concert
in Rome, so I like them
but their ambient music is not good if I need
music recommendation for my running session!

a real-world recommendation
engine needs to take into account
contextual information

what is context?
• Over 150 definitions, spread across several
domains (Bazire and Brezillon, 2005)
• Recommender Systems area
• “A set of factors that have influence on user
perception and acceptance of a
particular item”
• A fixed set of dimensions with appropriate
attributes

what is context?
task

what is context?
task company

what is context?
task moodcompany

what is context?
task company mood

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

timeline
2013: contextual eVSM
2010-2012: eVSM(*)
a content-based recommendation framework
based on distributional semantics
context-aware extension of eVSM
(*) Cataldo Musto: Enhanced vector space
models for content-based recommender
systems. RecSys 2010: 361-364

eVSM
a brief recap

eVSM
• Content-based Recommendation Framework
• (semantic) vector space representation based on
distributional models
• negative information modeled through quantum
negation operator
• recommendation seen as a form of similarity in vector
spaces
• four different proﬁling models
• cosine similarity to get the top-k recommendations
cornerstones

(Firth, 1957)
Firth, J.R. A synopsis of linguistic theory
1930-1955. In Studies in Linguistic Analysis,
pp. 1-32, 1957.
cornerstone 1

“meaning
is its use”
L.Wittgenstein
(Austrian philosopher)
distributional semantics

insight
by analyzing large corpus of textual data it is possible
to infer information about the usage (about the meaning)
of the terms.
example

distributional hypothesis
“terms that occur in similar
contexts share a similar meaning”

c1 c2 c3 c4 c5 c6
rock ✔ ✔ ✔
post rock ✔ ✔
jazz ✔
classical ✔ ✔ ✔
term/context matrix (WordSpace)

rock vs. post rock = good overlap
c1 c2 c3 c4 c5 c6
rock ✔ ✔ ✔
post rock ✔ ✔
jazz ✔

rock vs. classical = no overlap
c1 c2 c3 c4 c5 c6
rock ✔ ✔ ✔
post rock ✔ ✔
jazz ✔

• Key: deﬁnition of what is the
‘context’
• Different granularities
are possible
• Document
• Paragraph
• Sentence
• Sliding window of words

rock
alternative-rock
post-rock
classical
example
WordSpace

representation of documents (*)
can be inferred by combining the representation of
the terms (**) occurring in the document.
(*) documents = artists
(**) terms = tags

c1 c2 c3 c4 c5 c6 c7 c8 c9
t2 ✔ ✔ ✔ ✔
t3 ✔ ✔ ✔
d1 ✔ ✔ ✔ ✔ ✔
term/context matrix (DocSpace)

Coldplay
Radiohead
Kings of Leon
Lady Gaga
example
DocSpace

• Features
• semantic vector space representation of
terms and documents (user proﬁles and items !)
• light semantics, based on term co-occurrences in
large corpus of data
• based on distributional hypothesis
• totally unsupervised
• just based on the analysis of term distribution

quantum negation
(Widdows, 2007)
cornerstone 2

negation inVSMs
•Widdows proposed a different point
of view
• Negation view as a form of orthogonality
between vectors
• Vision inherited from Quantum Logic
state of the art

negation inVSMs
• Some theory
• Given vector a and vector b
• Through quantum negation it is possible to deﬁne a
vector a not b (a ∧¬b)
• Projection of vector a on the subspace
orthogonal to those generated by vector b
Quantum Negation

negation inVSMs
• Two terms can be considered as mutually unrelevant
if they never co-occur
• Two documents can be considered as mutually
unrelevant if they have no features in common
• No features in common scalar product = 0
•Orthogonality
Widdows’ insights

quantum negation
• Vector A models positive feedbacks
• Information about what a user likes
• Vector B models negative feedbacks
• Information about what a user does not like
• Vector A not B combines both information
sources
application to CBRS

recommendation step
cornerstone 3

ThresholdRatingItems
Higher weight given to the documents with higher rating
User Proﬁles
Weighted Random Indexing-based (w-RI)

VSM representation of wQN-based profile for user u
Positive User ProfileVector
Negative User ProfileVector
User Profiles
Weighted Quantum Negation-based (w-QN)

scenario
football news
sports news
politics news
politics news
user proﬁle
Recommendation
task seen as
similarity
calculation
between vectors

scenario
football news
sports news
politics news
politics news
user proﬁle
football and
sports news are
recommended to
the target user

size=400 - Movielens dataset
Gap always around 1%
84
84,75
85,5
86,25
87
p@1 P@3 P@5 P@10
84,7584,7
84,97
85,39
84,58
84,7
84,85
85,27
84,5
84,77
84,4384,47
85,5285,58
86,0185,94
eVSM VSM
LSI Bayes
experiment(*)
Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
(*) Cataldo Musto. Enhanced
Vector Space Models for
content-based
Recommender Systems.
Ph.D dissertation, 2012

84
84,75
85,5
86,25
87
p@1 P@3 P@5 P@10
84,7584,7
84,97
85,39
84,58
84,7
84,85
85,27
84,5
84,77
84,4384,47
85,5285,58
86,0185,94
eVSM VSM
LSI Bayes
size=400 - Movielens dataset
Signiﬁcant Improvement
Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
experiment(*) (*) Cataldo Musto. Enhanced
Vector Space Models for
content-based
Recommender Systems.
Ph.D dissertation, 2012

eVSM outperforms other state-of-the-art approaches

eVSM outperforms other state-of-the-art approaches
but it doesn’t take into account contextual information.

contextual eVSM
how to make the eVSM context-aware?

context-aware RSs
state of the art: pre and post-ﬁltering

we implemented two
contextualization strategies
both pre and post-ﬁltering

context-aware eVSM
•Microprofiling
•based on contextual pre-
filtering
•Insight: to filter data before building
user profiles, building a separate
user profile for each contextual
dimension

context-aware eVSM
•Contextual eVSM
•based on contextual post-
ﬁltering
•Insight: to calculate un-
contextualized recommendation
and to re-rank them according
to contextual constraints

if a user needs suggestions for a restaurant for a
romantic dinner, only her ratings expressed for
previous romantic dinners have to be taken
into account
micro-proﬁling
insight

micro-proﬁling
context-aware eVSM

micro-proﬁling
context-aware eVSM
user

micro-proﬁling
context-aware eVSM
contextual dimension (e.g task)
and value (e.g. running)

micro-proﬁling
context-aware eVSM
sum over all the items rated under that
speciﬁc contextual constraints

micro-proﬁling
context-aware eVSM
weighted with the (normalized) rating
provided by the user

contextual eVSM
insight
context is just a factor that can (positively or
negatively) inﬂuence user preference on a certain item

contextual eVSM
insight
context is just a factor that can (positively or
negatively) inﬂuence user preference on a certain item
context-aware recommendation can be obtained by
combining an uncontextual user proﬁle with a
vector space representation of the target context

contextual eVSM
context-aware eVSM

contextual eVSM
context-aware eVSM
user proﬁle is the combination of two components

contextual eVSM
context-aware eVSM
(1) uncontextual user proﬁle

contextual eVSM
context-aware eVSM
(2) vector space representation
of the context

contextual eVSM
context-aware eVSM
weight of each component

contextual eVSM
context-aware eVSM
if alpha = 1, we have
uncontextual recommendation
since context weight = 0 !

contextual eVSM
context-aware eVSM
problem: how to build it ?

• For each contextual dimension, it exists a
set of terms that is descriptive of items
relevant in that speciﬁc context
context representation
assumptions

• For each contextual dimension, it exists a set of
terms that is descriptive of items relevant in
that speciﬁc context
• e.g. candlelight or seaview are relevant features if
I’m looking for a restaurant for a romantic night !
assumptions

idea
to represent the context as the combination
of the terms occurring in the descriptions of the
items labeled as relevant under that speciﬁc
contextual situation

formula
PRE-WRI is exactly the combination of the terms
occurring in the descriptions of the items labeled as
relevant under that speciﬁc contextual situation !
we adopted PRE-WRI as vector space representation of
the context

rationale
contextual eVSM
if the user didn’t express any preference in that
speciﬁc context, the vector is null, so she will receive

rationale
contextual eVSM
if the user didn’t express any preference in that
speciﬁc context, the vector is null, so she will receive
otherwise, a greater weight will be given to the
features that are informative and relevant in the
target context, that is to say, those contained in the
context vector!

experimental
evaluation.

•Experiments
• (1) Does contextual eVSM outperform eVSM ?
• (2) How does our approach performs with respect to
current literature?
experimental design

• Movie recommendation (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)
experimental design
dataset

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

• eVSM settings
• Uncontextual baselines
• WRI and WQN
• Pre-ﬁltering
• PRE-WRI and PRE-WQN
• Post-ﬁltering:
• CONTEXT-WRI and CONTEXT-WQN
• alpha=0.5, alpha=0.8
• 8 settings for each run
experimental design
parameters

experiment 1
selection of results - HOME segment
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48,75 52,5 56,25 60
pre-ﬁltering outperforms the baseline
(it’s an exception, in the other segments it doesn’t !)

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48,75 52,5 56,25 60
contextual eVSM improves the F1 measure

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48,75 52,5 56,25 60
alpha=0.8 better than alpha 0.5

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48,75 52,5 56,25 60
contextual eVSM with negation is the best setting

experiment 1
selection of results - FRIEND segment
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,5 50 52,5 55
similar outcomes

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,5 50 52,5 55
pre-ﬁltering doesn’t improve the accuracy

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,5 50 52,5 55
contextual eVSM generally does

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,5 50 52,5 55
contextual eVSM with negation and alpha=0.8 is always the best setting

experiment 1
selection of results - NON RELEASE segment
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48 51 54 57
contextual eVSM with negation and alpha=0.8 is always the best setting

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48 51 54 57
typically, alpha=0.8 is better than alpha 0.5

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48 51 54 57
outcome: context has just a little inﬂuence on user perception and
acceptance. Uncontextual preferences are still the “core”.

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 48 51 54 57
outcome: context has to be taken into account, but just a little!

experiment 1
selection of results - THEATHER segment
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,25 49,5 51,75 54
However, in some setting
contextual eVSM without negation is the best

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,25 49,5 51,75 54
experiments showed a clear relationship between the amount of
negative ratings and the best conﬁgurations

experiment 1
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
45 47,25 49,5 51,75 54
when the dataset is well balanced, cont-WQN outperforms cont-WRI,
otherwise when few negative data are available, cont-WRI is the best

experiment 1
selection of results - THEATHER+FRIENDS segment
WRI
PRE-WRI
CONT-WRI-0.5
CONT-WRI-0.8
WQN
PRE-WQN
CONT-WQN-0.5
CONT-WQN-0.8
40 41,75 43,5 45,25 47
contextual eVSM didn’t improve F1 measure
in just 1 contextual segment out of 9

experiment 1 - outcome
contextual eVSM improves the predictive accuracy of eVSM

experiment 2
comparison with state-of-the-art
HOME
FRIENDS
WEEKEND
THEATHER
NONRELEASE
WEEKDAY
GBFRIEND
THEAT-WEEK
THEAT-FRIENDS
37 45,25 53,5 61,75 70
comparison with Adomavicius’ approach based on CF

experiment 2
comparison with state-of-the-art
HOME
FRIENDS
WEEKEND
THEATHER
NONRELEASE
WEEKDAY
GBFRIEND
THEAT-WEEK
THEAT-FRIENDS
37 45,25 53,5 61,75 70
eVSM outperforms CF in 6 segments out of 9

experiment 2 - outcome
contextual eVSM improves the predictive accuracy
of state-of-the-art approaches based on CF

recap.

recap
• context-aware eVSM
• baseline: eVSM
• content-based recommendation framework
• distributional semantics + quantum negation
• evolution: contextual eVSM
• comparison of two approaches for context-aware eVSM
• exact pre-ﬁltering and weighted post-ﬁltering
• large experimental evaluation, comparison with state of the
art approaches

contextual eVSM overcomes
state-of-the-art approaches

future research.

evaluation with
different datasets

open knowledge sources and
linked data for CBRS.

evaluation with user-based metrics
(serendipity, novelty, unexpectedness)

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

Contextual eVSM: a context-aware content-based recommendation framework based on distributional semantics

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Contextual eVSM: a context-aware content-based recommendation framework based on distributional semantics