contextual evsm: a context-aware content-based recommendation framework based on distributional...

EC-WEB 2013 - 14th International Conference on Electronic Commerce and Web Technologies 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 conferenceC. 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

dinner timeC. 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

me and Pasquale like sushi.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

what does TripAdvisor suggest?

Good PriceC. 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

Good ReviewsC. 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

....Strasbourg?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

612 km far away.

612 km far away.Too much :-)

we ate pizza, as usual.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

why did the TripAdvisor 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

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 company

what is context?

task company

what is context?

task moodcompany

what is context?

task company mood

a real-world recommendation engine needs to take into account

contextual information

Our contribution

contextual eVSMa 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

eVSMa brief recap

• 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

distributional models(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

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

distributional models

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)

distributional modelsrock vs. post rock = good overlap

c1 c2 c3 c4 c5 c6

rock ✔ ✔ ✔

post rock ✔ ✔

jazz ✔

distributional modelsrock 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

rockalternative-rock

post-rock

classical

exampleWordSpace

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

RadioheadKings of Leon

Lady Gaga

exampleDocSpace

• 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 in VSMs

•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 in VSMs

• 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 in VSMs

• 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 ProfilesWeighted Random Indexing-based (w-RI)

VSM representation of wQN-based profile for user u

Positive User Profile Vector

Negative User Profile Vector

User ProfilesWeighted Quantum Negation-based (w-QN)

scenario

football news

sports news

politics news

user profile

Recommendation task seen as similarity

calculation between vectors

scenario

football news

sports news

politics news

user profile

football and sports news are recommended to

the target user

size=400 - Movielens dataset

Gap always around 1%

p@1 P@3 P@5 P@10

84,7584,7

84,5884,7

84,4384,47

85,5285,58

86,0185,94eVSM VSMLSI 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

p@1 P@3 P@5 P@10

84,7584,7

84,5884,7

84,4384,47

85,5285,58

86,0185,94eVSM VSMLSI 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

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-profilinginsight

micro-profilingcontext-aware eVSM

contextual dimension (e.g task)and value (e.g. running)

sum over all the items rated under that specific contextual constraints

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 eVSMcontext-aware eVSM

user profile is the combination of two components

(1) uncontextual user profile

(2) vector space representation of the context

weight of each component

if alpha = 1, we have uncontextual recommendation since context weight = 0 !

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 representationassumptions

• 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 !

context representationassumptions

context representationidea

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

context representationformula

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

rationalecontextual eVSM

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

uncontextual recommendation

rationalecontextual eVSM

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

uncontextual recommendationotherwise, 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 designdataset

• Dataset and experimental settings replicate Adomavicius’ experiment (*)

• Evaluation over 9 different contextual settings

• Home, Friends, Non-release, Weekend, Weekday, GBFriends, TheatherWeekend and TheatherFriends

• Experimental protocol: bootstrapping

• 29/30th of the data as training

• 1/30th as test

• Randomly generated, 500 runs

experimental designsettings

(*) 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 designparameters

experiment 1selection of results - HOME segment

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 !)

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

PRE-WQN

CONT-WQN-0.5

CONT-WQN-0.8

45 48,75 52,5 56,25 60

contextual eVSM improves the F1 measureC. 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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

PRE-WQN

CONT-WQN-0.5

CONT-WQN-0.8

45 48,75 52,5 56,25 60

alpha=0.8 better than alpha 0.5C. 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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 settingC. 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

experiment 1selection of results - FRIEND segment

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

PRE-WQN

CONT-WQN-0.5

CONT-WQN-0.8

45 47,5 50 52,5 55

similar outcomes

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

PRE-WQN

CONT-WQN-0.5

CONT-WQN-0.8

45 47,5 50 52,5 55

pre-filtering doesn’t improve the accuracyC. 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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

PRE-WQN

CONT-WQN-0.5

CONT-WQN-0.8

45 47,5 50 52,5 55

contextual eVSM generally does

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 1selection of results - NON RELEASE segment

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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”.

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 1selection of results - THEATHER segment

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 1selection of results - THEATHER+FRIENDS segment

PRE-WRI

CONT-WRI-0.5

CONT-WRI-0.8

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 - outcomecontextual eVSM improves the predictive accuracy of eVSM

experiment 2comparison with state-of-the-art

FRIENDS

WEEKEND

THEATHER

NONRELEASE

WEEKDAY

GBFRIEND

THEAT-WEEK

THEAT-FRIENDS

37 45,25 53,5 61,75 70

comparison with Adomavicius’ approach based on CFC. 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

experiment 2comparison with state-of-the-art

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 - outcomecontextual 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

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 - 28.08.2013C. 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

contextual eVSM overcomes state-of-the-art approaches

future research.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

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.itC. 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

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