2 research outputs found
Cross-domain recommender system using Generalized Canonical Correlation Analysis
Recommender systems provide personalized recommendations to the users from a
large number of possible options in online stores. Matrix factorization is a
well-known and accurate collaborative filtering approach for recommender
system, which suffers from cold-start problem for new users and items. Whenever
a new user participate with the system there is not enough interactions with
the system, therefore there are not enough ratings in the user-item matrix to
learn the matrix factorization model. Using auxiliary data such as users
demographic, ratings and reviews in relevant domains, is an effective solution
to reduce the new user problem. In this paper, we used data of users from other
domains and build a common space to represent the latent factors of users from
different domains. In this representation we proposed an iterative method which
applied MAX-VAR generalized canonical correlation analysis (GCCA) on users
latent factors learned from matrix factorization on each domain. Also, to
improve the capability of GCCA to learn latent factors for new users, we
propose generalized canonical correlation analysis by inverse sum of selection
matrices (GCCA-ISSM) approach, which provides better recommendations in
cold-start scenarios. The proposed approach is extended using content-based
features from topic modeling extracted from users reviews. We demonstrate the
accuracy and effectiveness of the proposed approaches on cross-domain ratings
predictions using comprehensive experiments on Amazon and MovieLens datasets
Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach
Online streaming services have become the most popular way of listening to
music. The majority of these services are endowed with recommendation
mechanisms that help users to discover songs and artists that may interest them
from the vast amount of music available. However, many are not reliable as they
may not take into account contextual aspects or the ever-evolving user
behavior. Therefore, it is necessary to develop systems that consider these
aspects. In the field of music, time is one of the most important factors
influencing user preferences and managing its effects, and is the motivation
behind the work presented in this paper. Here, the temporal information
regarding when songs are played is examined. The purpose is to model both the
evolution of user preferences in the form of evolving implicit ratings and user
listening behavior. In the collaborative filtering method proposed in this
work, daily listening habits are captured in order to characterize users and
provide them with more reliable recommendations. The results of the validation
prove that this approach outperforms other methods in generating both
context-aware and context-free recommendation