Content - Based Recommendations Enhanced with Collaborative Information

Content Based Recommendations Enhanced with Collaborative Information

POLITECNICO DI MILANO

Scuola di Ingegneria Industriale e dell'Informazione

Corso di Laurea Magistrale in Ingegneria Informatica

Anno Accademico 2014 – 2015

Candidato: Alessandro Liparoti (819828)

Relatore: Prof. Paolo Cremonesi

RECOMMENDER SYSTEMS

software tools which analyze different source of data in order to predict the rating that a user would give to an item

Main Families:

• Collaborative Filtering• Content-based Filtering• Hybrid algorithms


COLLABORATIVE FILTERING

Collaborative Filteringassumption: users who agreed in the past will also agree in the

future

analyze past users’ ratings to compute predictions

User-Rating-Matrix (URM) is the rating given by user u to item i


✔ good performances

✘ not applicable if no enough ratings

for both users and items (cold-start problems)

CONTENT-BASED FILTERING

Content-Based Filteringassumption: users will like items similar to those they liked in the

past

compute items similarities’ scores considering item features

Item-Content-Matrix (ICM) if item i hasthe feature k


✔ no need of items’ ratings (i.e. works in a new-item scenario)

✘ ignoring users’relations leads to worse performances

HYBRID ALGORITHMS• new-item recommendations

- Factorization Machines (FM)- generic factorization model- can represent different types of models

- UFSM- computes item similarities as a CB approach- uses collaborative data to personalize them for each user

• no new-item recommendations - SSLIM

- learns a matrix of item-item coefficients- improves SLIM adding side information


GOAL OF THE THESIS

usual hybridization use item content data to improve collaborative models

our hybridization build a content-based model enhanced with collaborative data

CONTENT-BASED COLLABORATIVE


✔exploits collaborative data (also in a new-item scenario)

✔uses weigths for features and user-feature relations

CONTENT BASED COLLABORATIVE

content-based similarity function

CBC similarity function

• bk control the importance of feature k(e.g. usually genre > year of production for movies)

• cu,k control the importance of the relation between user u and feature k

(e.g. a user likes a particular actor)POLITECNICO DI MILANO

CBC VERSIONS

rating prediction

CBCrmse

partial effects:analytical sequential approach

item recommendation

CBCbpr

stochastic gradient descent approach


CBC parameters θ are learned minimizing an error function

DATASET


HetRec2011-Movielens

RecSys-Polimi IMDB

SIMULATIONS


URM was split in three parts

two types of simulation

collaborative train on A+B test on C

new-item train on A test on C

TESTING


- rating prediction metricsRMSE

RMSEp (only on positive ratings)

- item recommendation metricsprecision

recall

mean average precision (MAP)

mean reciprocal rank (MRR)

normalized discounted cumulative gain (NDCG)

ALGORITHMS OF COMPARISON


• Collaborative - Matrix Factorization (MF)

Asymmetric-SVD

Bayesian Personalized Ranking MF

• Hybrid

Factorization Machines

• Content-Based

item k-nearest-neighbor

RESULTS


collaborative simulations

RESULTS – ITEM RECOMMENDATION


new-item simulations

THINGS TO DO


use weights to control the importance of relations among items’ features

(e.g. two actors appearing together in many movies)

use a CBC-like similarity function to compare users instead of items

(e.g. gender, age, demographic information,…)

collaborative method (BPR-MF) not applicable

CBC is the best algorithm

THE END


THANK YOU

Data & Analytics

Content - Based Recommendations Enhanced with Collaborative Information