171 research outputs found
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes
are available at https://github.com/AHosseini/RP
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
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of
user-item interactions over time. There are two crucial factors when modelling
user preferences for link prediction in dynamic interaction graphs: 1)
collaborative relationship among users and 2) user personalized interaction
patterns. Existing methods often implicitly consider these two factors
together, which may lead to noisy user modelling when the two factors diverge.
In addition, they usually require time-consuming parameter learning with
back-propagation, which is prohibitive for real-time user preference modelling.
To this end, this paper proposes FreeGEM, a parameter-free dynamic graph
embedding method for link prediction. Firstly, to take advantage of the
collaborative relationships, we propose an incremental graph embedding engine
to obtain user/item embeddings, which is an Online-Monitor-Offline architecture
consisting of an Online module to approximately embed users/items over time, a
Monitor module to estimate the approximation error in real time and an Offline
module to calibrate the user/item embeddings when the online approximation
errors exceed a threshold. Meanwhile, we integrate attribute information into
the model, which enables FreeGEM to better model users belonging to some under
represented groups. Secondly, we design a personalized dynamic interaction
pattern modeller, which combines dynamic time decay with attention mechanism to
model user short-term interests. Experimental results on two link prediction
tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy
while achieving over 36X improvement in efficiency. All code and datasets can
be found in https://github.com/FudanCISL/FreeGEM.Comment: 19 pages, 9 figures, 13 tables, Thirty-Sixth Conference on Neural
Information Processing Systems (NeurIPS 2022), preprint versio
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
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