Extending Recommendation Systems With Semantics And Context Awareness

Extending recommendation systems with semantics and context-awareness

CCIA 2011

Victor Codina & Luigi Ceccaroni

[email protected] [email protected]

Departament de Llenguatges i Sistemes Informàtics

Knowledge Engineering and Machine Learning Group

Health Informatics

Personalized Computational Medicine

Outline

Traditional vs. Contextual recommendation

State-of-the-art & Current limitations

Research question

Semantics acquisition & exploitation

Proposed model

Experimental evaluation

Conclusions & Future work

Extending Recommendation Systems with Semantics and Context-Awareness 2

Regression problem:

o Given a pair (u ∈ U, i ∈ I), predict item’s degree of utility ( )

Estimation based only on user and item information

Traditional recommendation problem


Recommendation model

preferences (u)

attributes (i)Preference

MatrixContent-based (CB)

recommenderCollaborative filtering (CF)

recommenderHybrid

recommender

Context as additional dimension for estimationo Given a tuple (u, i, c), predict item’s degree of utility in context c

o Context = “situated action”

Context-aware recommendation problem


Recommendation model

Training data

Pre-filteringc

Post-filtering

Multi-Dimensional (MD)c

c

Representational view:

Example:c = (winter, cold)c1 = Season c2 = Temperature

State-of-the-art & limitations

Adaptations of latent-factor models (MD paradigm)

Examples:

o N-dimensional Tensor Factorization

o Bias-based Matrix Factorization with temporal dynamics

Best prediction accuracy results on recent competitions

o E.g.: Netflix challenge (2009), Yahoo! Labs KDD Cup (2011)

Main limitations of latent-factor models:

o Lack of transparency in explaining recommendations

o Low cold-start performance (users and items with few ratings)

o Lack of novelty and diversity of recommendations


Research questions & main assumptions

Research questions

o Q1. Can we overcome the limitations and improve global recommendation quality (not only prediction accuracy) by exploiting domain and context knowledge?

o Q2. Under which conditions is this improvement maximized?

Main assumptions

o There exists semantic relationships among entities of the recommendation space (users, items, contexts)

o The adequate exploitation of these semantic relationships is useful to overcome current limitations


Explicit similarity

Knowledge acquisition and representation


Concept x

S(x,y)?

Concept y

Ontology-based - Edge-based (LCA)- Node-based (MICA)- Logic-based

Statistics-based - Probabilistic measures (PMI)- Dimensionality reduction (LSA)- Graph-based (SimRank)

Domain/Contextconcepts

Similarity measure

Knowledge source

Implicit similarity

Data collections- Folksonomies- Item descriptions

Ontologies- Taxonomies (ODP)- Thesauri (Wordnet)

uses uses

Concept-based modeling (weighted overlay approach)

User/Item representation


d1

d2

d3

d4

Domain knowledge (concepts = item attributes)

Item ‘i’

Pi

User ‘u’

Pu

(Relevance of d3)(Degree of interest in d1)

Interest inferring method- Explicit feedback (Rating avg)- Implicit feedback (Seen frequency)

Attribute weighting method- Structured content (IDF)- Unstructured (TFIDF, tagshare)

Knowledge exploitation


Knowledgetype

Can be used for… Possible benefits

Contextual

Measuring the semantic matching among different context states

Less rigid contextual filtering than using exact matching

Domain-based

Applying semantic inference methods over user/item concept-based profiles:

- Spreading activation- Reasoning based on DLs

Enrich item/user profiles with new concepts semantically related

Measuring the matching between two user/item using various semantic matching strategies:

- Pairwise (Best-pairs or All-pairs)- Groupwise (set-, vector- or graph-based)

More precisesimilarity measurements that using traditional measures

Case of study: a MD semantically-enhanced CB

Contextual prediction model (bias-based):

where:

Stochastic gradient descent for model training:


Contextual User bias

Contextual Item bias All-pairs Item-User semantic matching

Overallrating avg

Session bias of (u,d) contextual bias of (u,d)

MovieLens Dataset

Contextual concepts without semantics

o 3 contextual factors (season, time of the day, weekend?)

Domain concepts with implicit semantics

o Set of pre-selected tags + set of genres

o Semantic relationships among tags acquired from folksonomy

Original dataset pruned by selecting only items with a certain amount of pre-selected tags


Offline experiment

Last ratings (according to timestamp) testing

o In this way we simulate future predictions for each user

5-fold cross validation

Two recommendation tasks evaluated

o Rating prediction (RMSE) and Top-10 recommendation (Recall)

Threshold-based cold-start performance evaluation

o User profile size < 25 ratings: 10% of users

Performance comparison of the proposed model with:

o 3 model variants

o 5 baseline models

o 1 model based on matrix factorization


Results

Paired t test significance among 4 model variants:o Model 1 “Static-CB” (static bias + traditional Item-User matching)

o Model 2 “Static-SemCB” (static bias + “All-pairs” matching)

o Model 3 “Contextual-CB” (contextual bias + traditional matching)

o Model 4 “Contextual-SemCB” (contextual bias + “All-pairs” matching)


0,05 0,001 0,17

0,83

0,837

0,844

0,851

Model 1 Model 2 Model 3 Model 4

Global RMSE

0,05 0,62 0,01

0,916

0,917

0,918

0,919

Model 1 Model 2 Model 3 Model 4

Cold-Start RMSE

(P-values in red)E.g. P-value = 0,05 means that there is a 95% chance of being a real difference

Conclusions

Context-awareness improves prediction accuracy for users with a certain number of ratings (non cold-start)

o 25+ rating: 90% of users

Semantics slightly improves cold-start performance

The knowledge acquisition method for the MovieLens folksonomy may be not adequate: limited domain knowledge

MovieLens users rate several movies at once and not just after seeing the movie

o Rating-session--specific effects have a major influence in the user ratings: distorted contextual information


Future work

Extending evaluation of the proposed CB model:

o Using datasets from other domains (e.g. music, tourism, health)

o Experimenting with other sources of knowledge (e.g. Amazon movie taxonomy)

o Experimenting with other methods for semantics exploitation

o Evaluating other properties (e.g. diversity, novelty, coverage)

Extending CF models with the proposed semantic approach:

o Neighborhood-based

o Matrix Factorization


Extending recommendation systems with semantics and context-awareness

CCIA 2011

Victor Codina & Luigi Ceccaroni

[email protected] [email protected]

Departament de Llenguatges i Sistemes Informàtics

Knowledge Engineering and Machine Learning Group

Health Informatics

Personalized Computational Medicine

Backup slides


Prediction models of all variants

Model 1 (Static-CB):

Model 2 (Static-SemCB):

Model 3 (Contextual-CB):

Model 4 (Contextual-SemCB):


Documents

Extending Recommendation Systems With Semantics And Context Awareness