4,269 research outputs found
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
Discreetly Exploiting Inter-session Information for Session-based Recommendation
Limited intra-session information is the performance bottleneck of the early
GNN based SBR models. Therefore, some GNN based SBR models have evolved to
introduce additional inter-session information to facilitate the next-item
prediction. However, we found that the introduction of inter-session
information may bring interference to these models. The possible reasons are
twofold. First, inter-session dependencies are not differentiated at the
factor-level. Second, measuring inter-session weight by similarity is not
enough. In this paper, we propose DEISI to solve the problems. For the first
problem, DEISI differentiates the types of inter-session dependencies at the
factor-level with the help of DRL technology. For the second problem, DEISI
introduces stability as a new metric for weighting inter-session dependencies
together with the similarity. Moreover, CL is used to improve the robustness of
the model. Extensive experiments on three datasets show the superior
performance of the DEISI model compared with the state-of-the-art models
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Streaming session-based recommendation (SSR) is a challenging task that
requires the recommender system to do the session-based recommendation (SR) in
the streaming scenario. In the real-world applications of e-commerce and social
media, a sequence of user-item interactions generated within a certain period
are grouped as a session, and these sessions consecutively arrive in the form
of streams. Most of the recent SR research has focused on the static setting
where the training data is first acquired and then used to train a
session-based recommender model. They need several epochs of training over the
whole dataset, which is infeasible in the streaming setting. Besides, they can
hardly well capture long-term user interests because of the neglect or the
simple usage of the user information. Although some streaming recommendation
strategies have been proposed recently, they are designed for streams of
individual interactions rather than streams of sessions. In this paper, we
propose a Global Attributed Graph (GAG) neural network model with a Wasserstein
reservoir for the SSR problem. On one hand, when a new session arrives, a
session graph with a global attribute is constructed based on the current
session and its associate user. Thus, the GAG can take both the global
attribute and the current session into consideration to learn more
comprehensive representations of the session and the user, yielding a better
performance in the recommendation. On the other hand, for the adaptation to the
streaming session scenario, a Wasserstein reservoir is proposed to help
preserve a representative sketch of the historical data. Extensive experiments
on two real-world datasets have been conducted to verify the superiority of the
GAG model compared with the state-of-the-art methods
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