16,807 research outputs found
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author
SSLRec: A Self-Supervised Learning Framework for Recommendation
Self-supervised learning (SSL) has gained significant interest in recent
years as a solution to address the challenges posed by sparse and noisy data in
recommender systems. Despite the growing number of SSL algorithms designed to
provide state-of-the-art performance in various recommendation scenarios (e.g.,
graph collaborative filtering, sequential recommendation, social
recommendation, KG-enhanced recommendation), there is still a lack of unified
frameworks that integrate recommendation algorithms across different domains.
Such a framework could serve as the cornerstone for self-supervised
recommendation algorithms, unifying the validation of existing methods and
driving the design of new ones. To address this gap, we introduce SSLRec, a
novel benchmark platform that provides a standardized, flexible, and
comprehensive framework for evaluating various SSL-enhanced recommenders. The
SSLRec framework features a modular architecture that allows users to easily
evaluate state-of-the-art models and a complete set of data augmentation and
self-supervised toolkits to help create SSL recommendation models with specific
needs. Furthermore, SSLRec simplifies the process of training and evaluating
different recommendation models with consistent and fair settings. Our SSLRec
platform covers a comprehensive set of state-of-the-art SSL-enhanced
recommendation models across different scenarios, enabling researchers to
evaluate these cutting-edge models and drive further innovation in the field.
Our implemented SSLRec framework is available at the source code repository
https://github.com/HKUDS/SSLRec.Comment: Published as a WSDM'24 full paper (oral presentation
- …