6,715 research outputs found
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Recommender systems aim to enhance the overall user experience by providing
tailored recommendations for a variety of products and services. These systems
help users make more informed decisions, leading to greater user satisfaction
with the platform. However, the implementation of these systems largely depends
on the context, which can vary from recommending an item or package to a user
or a group. This requires careful exploration of several models during the
deployment, as there is no comprehensive and unified approach that deals with
recommendations at different levels. Furthermore, these individual models must
be closely attuned to their generated recommendations depending on the context
to prevent significant variation in their generated recommendations. In this
paper, we propose a novel unified recommendation framework that addresses all
four recommendation tasks, namely personalized, group, package, or
package-to-group recommendation, filling the gap in the current research
landscape. The proposed framework can be integrated with most of the
traditional matrix factorization-based collaborative filtering models. The idea
is to enhance the formulation of the existing approaches by incorporating
components focusing on the exploitation of the group and package latent
factors. These components also help in exploiting a rich latent representation
of the user/item by enforcing them to align closely with their corresponding
group/package representation. We consider two prominent CF techniques,
Regularized Matrix Factorization and Maximum Margin Matrix factorization, as
the baseline models and demonstrate their customization to various
recommendation tasks. Experiment results on two publicly available datasets are
reported, comparing them to other baseline approaches that consider individual
rating feedback for group or package recommendations.Comment: 25 page
Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation
Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a "top'' (e.g. a shirt) and a "bottom'' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain
Algorithms and Architecture for Real-time Recommendations at News UK
Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.Comment: Accepted for presentation at AI-2017 Thirty-seventh SGAI
International Conference on Artificial Intelligence. Cambridge, England 12-14
December 201
Adaptive Matrix Completion for the Users and the Items in Tail
Recommender systems are widely used to recommend the most appealing items to
users. These recommendations can be generated by applying collaborative
filtering methods. The low-rank matrix completion method is the
state-of-the-art collaborative filtering method. In this work, we show that the
skewed distribution of ratings in the user-item rating matrix of real-world
datasets affects the accuracy of matrix-completion-based approaches. Also, we
show that the number of ratings that an item or a user has positively
correlates with the ability of low-rank matrix-completion-based approaches to
predict the ratings for the item or the user accurately. Furthermore, we use
these insights to develop four matrix completion-based approaches, i.e.,
Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization
(TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse
Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional
matrix-completion-based approaches for the users and the items with few ratings
in the user-item rating matrix.Comment: 7 pages, 3 figures, ACM WWW'1
Scalable Recommendation with Poisson Factorization
We develop a Bayesian Poisson matrix factorization model for forming
recommendations from sparse user behavior data. These data are large user/item
matrices where each user has provided feedback on only a small subset of items,
either explicitly (e.g., through star ratings) or implicitly (e.g., through
views or purchases). In contrast to traditional matrix factorization
approaches, Poisson factorization implicitly models each user's limited
attention to consume items. Moreover, because of the mathematical form of the
Poisson likelihood, the model needs only to explicitly consider the observed
entries in the matrix, leading to both scalable computation and good predictive
performance. We develop a variational inference algorithm for approximate
posterior inference that scales up to massive data sets. This is an efficient
algorithm that iterates over the observed entries and adjusts an approximate
posterior over the user/item representations. We apply our method to large
real-world user data containing users rating movies, users listening to songs,
and users reading scientific papers. In all these settings, Bayesian Poisson
factorization outperforms state-of-the-art matrix factorization methods
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