mining semantic data for solving first-rater and cold-start problems in recommender systems
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IDEAS 2011 Lisbon 21-23 September. Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems. Data Mining Research Group http://mida.usal.es. María N. Moreno, Saddys Segrera , Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez. Department of - PowerPoint PPT PresentationTRANSCRIPT
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MINING SEMANTIC DATA FOR SOLVING FIRST-RATER AND COLD-START PROBLEMS IN RECOMMENDER SYSTEMSMaría N. Moreno, Saddys Segrera, Vivian F.
López, M. Dolores Muñoz and Ángel Luis Sánchez
Department of Computing and Automatic
Data Mining Research Grouphttp://mida.usal.es
IDEAS 2011Lisbon21-23 September
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Contents Introduction Recommender Systems Recommendation framework Case Study Conclusions
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Introduction
Client
Server
Catalog
commerce
Info
rmat
ion
Recommender systems
Applications: e-commerce, e-learning, tourism, news’ pages…
Drawbacks: low performance, low reliability of recommendations…
Recommender systems provide users with
intelligent mechanisms to find products to purchase
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Introduction Proposal
Objective: overcome critical drawbacks in recommender systems
Methodology: Semantic based Web Mining Associative classification (Web Mining)
Machine learning technique that combines concepts from classification and association
Domain-specific ontology (Semantic Web) Enrichment of the data to be mined with semantic
annotations
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Recommender Systems Classification of recommendation
methods Content-based: compare text documents
to user profiles Collaborative filtering: is based on
opinions of other users (ratings) Memory based (User-based): find users with
similar preferences (neighbors) by means of statistical techniques
Model based (Item-based): use data mining techniques to develop a model of user ratings
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Recommender Systems Critical drawbacks
Sparsity: the number of ratings needed for prediction is greater than the number of the ratings obtained from users
Scalability: performance problems presented mainly in memory-based methods where the computation time grows linearly with both the number of customers and the number of products in the site
First-rater problem: new products never have been rated, therefore they cannot be recommended
Cold-Start problem: new users cannot receive recommendations since they have no evaluations about products
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Recommendation framework
Associative classification (Web Mining) Sparsity: slightly sensitive to sparse data Scalability: model based approach
Domain-specific ontology (Semantic Web) First-rater problem:
Use of taxonomies to classify products Induction of abstracts patterns which relate user
profiles with categories of products Cold-Start problem:
Recommendations based on user profiles
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Recommendation framework
Historical data
Domain ontology
Historical data with semantic annotations
Low level model
High level model
Recommendation request
[new user]
[old user]
Registration
new products
old products
Recommendations
Off-line process
On-line process
Active user
Data mining algorithms
Provide annotations
Data mining algorithms
Check high level model
Check low level model
Check high level model
CEDI 2010
Case Study
User Data
ZipNum.
Movies Data
TitleString
IDNum.
Genre (19 attributes)Binary
IDNum.
Ratings Data
RatingNum. (1 - 5)
Movie IDNum.
User IDNum.
IDNum.
OccupationString
GenderBinary
AgeNum.
score rating_bin
MovieLens Data
CEDI 2010
Case Study
IDNum.
User GenderBinary
*User Age < 18 [18, 24] [25, 34] [35, 44] [45, 49] [50, 55] > 55
User OccupationString
Movie TitleString
*Movie Genre String
MovieLens Data
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Case Study Ontology definition
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Case Study Results
Associative classification methods (CBA, CMAR, FOIL and CPAR) were compared to non-associative classification algorithms
CEDI 2010
Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender SystemsMaría N. Moreno, Saddys Segrera, Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez
Conclusions A framework for recommender systems is proposed in
order to overcome some critical drawbacks The proposal combines web mining methods and domain
specific ontologies in order to induce models at two abstraction levels: The low level model relates users, movies and ratings for making
the recommendations High level model is used for recommender not rated movies or for
making recommendation to new users and overcome the first-rater and the cold-start problem
The off-line model induction avoids scalability problems in recommendation time
Associative classification methods provides a way to deal with sparsity problem
THANKS FOR YOUR ATTENTION !MINING SEMANTIC DATA FOR SOLVING FIRST-RATER
AND COLD-START PROBLEMS IN RECOMMENDER SYSTEMS
María N. Moreno*, Saddys Segrera, Vivian F. López, M. Dolores Muñoz & Ángel Luis Sánchez
Department of Computing and Automatic
IDEAS 2011Lisbon21-23 September