1,331 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
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh the
model with new interaction data. To pursue high model fidelity, it is usually
desirable to retrain the model on both historical and new data, since it can
account for both long-term and short-term user preference. However, a full
model retraining could be very time-consuming and memory-costly, especially
when the scale of historical data is large. In this work, we study the model
retraining mechanism for recommender systems, a topic of high practical values
but has been relatively little explored in the research community.
Our first belief is that retraining the model on historical data is
unnecessary, since the model has been trained on it before. Nevertheless,
normal training on new data only may easily cause overfitting and forgetting
issues, since the new data is of a smaller scale and contains fewer information
on long-term user preference. To address this dilemma, we propose a new
training method, aiming to abandon the historical data during retraining
through learning to transfer the past training experience. Specifically, we
design a neural network-based transfer component, which transforms the old
model to a new model that is tailored for future recommendations. To learn the
transfer component well, we optimize the "future performance" -- i.e., the
recommendation accuracy evaluated in the next time period. Our Sequential
Meta-Learning(SML) method offers a general training paradigm that is applicable
to any differentiable model. We demonstrate SML on matrix factorization and
conduct experiments on two real-world datasets. Empirical results show that SML
not only achieves significant speed-up, but also outperforms the full model
retraining in recommendation accuracy, validating the effectiveness of our
proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning
Sustaining users' interest and keeping them engaged in the platform is very
important for the success of an e-commerce business. A session encompasses
different activities of a user between logging into the platform and logging
out or making a purchase. User activities in a session can be classified into
two groups: Known Intent and Unknown intent. Known intent activity pertains to
the session where the intent of a user to browse/purchase a specific product
can be easily captured. Whereas in unknown intent activity, the intent of the
user is not known. For example, consider the scenario where a user enters the
session to casually browse the products over the platform, similar to the
window shopping experience in the offline setting. While recommending similar
products is essential in the former, accurately understanding the intent and
recommending interesting products is essential in the latter setting in order
to retain a user. In this work, we focus primarily on the unknown intent
setting where our objective is to recommend a sequence of products to a user in
a session to sustain their interest, keep them engaged and possibly drive them
towards purchase. We formulate this problem in the framework of the Markov
Decision Process (MDP), a popular mathematical framework for sequential
decision making and solve it using Deep Reinforcement Learning (DRL)
techniques. However, training the next product recommendation is difficult in
the RL paradigm due to large variance in browse/purchase behavior of the users.
Therefore, we break the problem down into predicting various product
attributes, where a pattern/trend can be identified and exploited to build
accurate models. We show that the DRL agent provides better performance
compared to a greedy strategy
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Reinforcement learning serves as a potent tool for modeling dynamic user
interests within recommender systems, garnering increasing research attention
of late. However, a significant drawback persists: its poor data efficiency,
stemming from its interactive nature. The training of reinforcement
learning-based recommender systems demands expensive online interactions to
amass adequate trajectories, essential for agents to learn user preferences.
This inefficiency renders reinforcement learning-based recommender systems a
formidable undertaking, necessitating the exploration of potential solutions.
Recent strides in offline reinforcement learning present a new perspective.
Offline reinforcement learning empowers agents to glean insights from offline
datasets and deploy learned policies in online settings. Given that recommender
systems possess extensive offline datasets, the framework of offline
reinforcement learning aligns seamlessly. Despite being a burgeoning field,
works centered on recommender systems utilizing offline reinforcement learning
remain limited. This survey aims to introduce and delve into offline
reinforcement learning within recommender systems, offering an inclusive review
of existing literature in this domain. Furthermore, we strive to underscore
prevalent challenges, opportunities, and future pathways, poised to propel
research in this evolving field.Comment: under revie
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
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