1,198 research outputs found
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target
spaces are characterised by a need for efficient prediction strategies that
avoid the computation of predictions for all targets explicitly. Examples of
such problems emerge in several subfields of machine learning, such as
collaborative filtering, multi-label classification, dyadic prediction and
biological network inference. In this article we analyse efficient and exact
algorithms for computing the top- predictions in the above problem settings,
using a general class of models that we refer to as separable linear relational
models. We show how to use those inference algorithms, which are modifications
of well-known information retrieval methods, in a variety of machine learning
settings. Furthermore, we study the possibility of scoring items incompletely,
while still retaining an exact top-K retrieval. Experimental results in several
application domains reveal that the so-called threshold algorithm is very
scalable, performing often many orders of magnitude more efficiently than the
naive approach
A Probabilistic Approach for Item Based Collaborative Filtering
In this era, it is essential to know the customer’s necessity before they know it themselves. The Recommendation system is a sub-class of machine learning which deals with the user data to offer relevant content or product to the user based on their taste. This paper aims to develop an integrated recommendation system using statistical theory and methods. Therefore, the conventional Item Based Collaborative filtering integrated the probabilistic approach and the pseudo-probabilistic approach is proposed to update the k-NN approach. Here we synthesize the data using the Monte-Carlo approach with the binomial and the multinomial distribution. Then we examine the performance of the proposed methodologies on the synthetic data using the RMSE calculation
Long-tail Augmented Graph Contrastive Learning for Recommendation
Graph Convolutional Networks (GCNs) has demonstrated promising results for
recommender systems, as they can effectively leverage high-order relationship.
However, these methods usually encounter data sparsity issue in real-world
scenarios. To address this issue, GCN-based recommendation methods employ
contrastive learning to introduce self-supervised signals. Despite their
effectiveness, these methods lack consideration of the significant degree
disparity between head and tail nodes. This can lead to non-uniform
representation distribution, which is a crucial factor for the performance of
contrastive learning methods. To tackle the above issue, we propose a novel
Long-tail Augmented Graph Contrastive Learning (LAGCL) method for
recommendation. Specifically, we introduce a learnable long-tail augmentation
approach to enhance tail nodes by supplementing predicted neighbor information,
and generate contrastive views based on the resulting augmented graph. To make
the data augmentation schema learnable, we design an auto drop module to
generate pseudo-tail nodes from head nodes and a knowledge transfer module to
reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ
generative adversarial networks to ensure that the distribution of the
generated tail/head nodes matches that of the original tail/head nodes.
Extensive experiments conducted on three benchmark datasets demonstrate the
significant improvement in performance of our model over the state-of-the-arts.
Further analyses demonstrate the uniformity of learned representations and the
superiority of LAGCL on long-tail performance. Code is publicly available at
https://github.com/im0qianqian/LAGCLComment: 17 pages, 6 figures, accepted by ECML/PKDD 2023 (European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in
Databases
Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions
The cold-start is the situation in which the recommender
system has no or not enough information about the (new) users/items, i.e. their ratings/feedback; hence, the recommendations are not accurate. Active learning techniques for recommender systems propose to interact
with new users by asking them to rate sequentially a few items while the system tries to detect her preferences. This bootstraps recommender systems and alleviate the new user cold-start. Compared to current state of the art, the presented approach takes into account the users' ratings
predictions in addition to the available users' ratings. The experimentation shows that our approach achieves better performance in terms of precision and limits the number of questions asked to the users
Exploiting past users’ interests and predictions in an active learning method for dealing with cold start in recommender systems
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences
to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Collaborative filtering (CF) is widely used to learn an informative latent
representation of a user or item from observed interactions. Existing CF-based
methods commonly adopt negative sampling to discriminate different items. That
is, observed user-item pairs are treated as positive instances; unobserved
pairs are considered as negative instances and are sampled under a defined
distribution for training. Training with negative sampling on large datasets is
computationally expensive. Further, negative items should be carefully sampled
under the defined distribution, in order to avoid selecting an observed
positive item in the training dataset. Unavoidably, some negative items sampled
from the training dataset could be positive in the test set. Recently,
self-supervised learning (SSL) has emerged as a powerful tool to learn a model
without negative samples. In this paper, we propose a self-supervised
collaborative filtering framework (SelfCF), that is specially designed for
recommender scenario with implicit feedback. The main idea of SelfCF is to
augment the output embeddings generated by backbone networks, because it is
infeasible to augment raw input of user/item ids. We propose and study three
output perturbation techniques that can be applied to different types of
backbone networks including both traditional CF models and graph-based models.
By encapsulating two popular recommendation models into the framework, our
experiments on three datasets show that the best performance of our framework
is comparable or better than the supervised counterpart. We also show that
SelfCF can boost up the performance by up to 8.93\% on average, compared with
another self-supervised framework as the baseline. Source codes are available
at: https://github.com/enoche/SelfCF
Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization
Collaborative filtering (CF) has become a popular method for developing
recommender systems (RS) where ratings of a user for new items is predicted
based on her past preferences and available preference information of other
users. Despite the popularity of CF-based methods, their performance is often
greatly limited by the sparsity of observed entries. In this study, we explore
the data augmentation and refinement aspects of Maximum Margin Matrix
Factorization (MMMF), a widely accepted CF technique for the rating
predictions, which have not been investigated before. We exploit the inherent
characteristics of CF algorithms to assess the confidence level of individual
ratings and propose a semi-supervised approach for rating augmentation based on
self-training. We hypothesize that any CF algorithm's predictions with low
confidence are due to some deficiency in the training data and hence, the
performance of the algorithm can be improved by adopting a systematic data
augmentation strategy. We iteratively use some of the ratings predicted with
high confidence to augment the training data and remove low-confidence entries
through a refinement process. By repeating this process, the system learns to
improve prediction accuracy. Our method is experimentally evaluated on several
state-of-the-art CF algorithms and leads to informative rating augmentation,
improving the performance of the baseline approaches.Comment: 20 page
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