Contact: Ernesto Diaz-Aviles, Mihai Georgescuemail: {diaz, georgescu}@L3S.de

Swarming to Rank for Recommender SystemsErnesto Diaz-Aviles, Mihai Georgescu, and Wolfgang Nejdl

• Address the item recommendation task in the context of recommender systems

• An approach to learning ranking functions exploiting collaborative latent factors as features

• Instead of manually creating an item feature vector, factorize a matrix of user-item interactions

•Use these collaborative latent factors as input to

the Swarm Intelligence(SI) ranking method SwarmRank

Overview

Evaluation

SI for Recommender Systems

Dataset: Real world data from internet radio:

5-core of the Last.fm Dataset –1K Users

transactions 242,103

Unique users 888

Items(artists) 35,315

Swarm-RankCF• a collaborative learning to rank algorithm based on SI• while learning to rank algorithms use hand-picked feature to represent items we learn such features based on user-item interactions, and apply a PSO-based optimization algorithm that directly maximizes Mean Average Precision.

Evaluation Methodology: All-but-one

protocol or leave-one-out holdout method

where hit(u) = 1, if the hidden item I is present in u’sTop-N list of recommendations, and 0 otherwise.

L3S Research Center / Leibniz Universität Hannover

Appelstrasse 4, 30167 Hannover, Germanyphone: +49 511 762-19715

www.cubrikproject.eu