43,854 research outputs found
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
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
CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference
The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the
world
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Cross-domain Recommendation (CR) has been extensively studied in recent years
to alleviate the data sparsity issue in recommender systems by utilizing
different domain information. In this work, we focus on the more general
Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is
challenging because there are no overlapped entities (e.g., users and items)
between domains, and there is only users' implicit feedback and no content
information. Previous CR methods cannot solve NCSR well, since (1) they either
need extra content to align domains or need explicit domain alignment
constraints to reduce the domain discrepancy from domain-invariant features,
(2) they pay more attention to users' explicit feedback (i.e., users' rating
data) and cannot well capture their sequential interaction patterns, (3) they
usually do a single-target cross-domain recommendation task and seldom
investigate the dual-target ones. Considering the above challenges, we propose
Prompt Learning-based Cross-domain Recommender (PLCR), an automated
prompting-based recommendation framework for the NCSR task. Specifically, to
address the challenge (1), PLCR resorts to learning domain-invariant and
domain-specific representations via its prompt learning component, where the
domain alignment constraint is discarded. For challenges (2) and (3), PLCR
introduces a pre-trained sequence encoder to learn users' sequential
interaction patterns, and conducts a dual-learning target with a separation
constraint to enhance recommendations in both domains. Our empirical study on
two sub-collections of Amazon demonstrates the advance of PLCR compared with
some related SOTA methods
Secondary implementation of interactive engagement teaching techniques: Choices and challenges in a Gulf Arab context
We report on a "Collaborative Workshop Physics" instructional strategy to
deliver the first IE calculus-based physics course at Khalifa University, UAE.
To these authors' knowledge, this is the first such course on the Arabian
Peninsula using PER-based instruction. A brief history of general university
and STEM teaching in the UAE is given. We present this secondary implementation
(SI) as a case study of a novel context and use it to determine if PER-based
instruction can be successfully implemented far from the cultural context of
the primary developer and, if so, how might such SIs differ from SIs within the
US. With these questions in view, a pre-reform baseline of MPEX, FCI, course
exam and English language proficiency data are used to design a hybrid
implementation of Cooperative Group Problem Solving. We find that for students
with high English proficiency, normalized gain on FCI improves from =
0.16+/-0.10 pre- to = 0.47+/-0.08 post-reform, indicating successful SI. We
also find that is strongly modulated by language proficiency and discuss
likely causes. Regardless of language skill, problem-solving skill is also
improved and course DFW rates drop from 50% to 24%. In particular, we find
evidence in post-reform student interviews that prior classroom experiences,
and not broader cultural expectations about education, are the more significant
cause of expectations at odds with the classroom norms of well-functioning
PER-based instruction. This result is evidence that PER-based innovations can
be implemented across great changes in cultural context, provided that the
method is thoughtfully adapted in anticipation of context and culture-specific
student expectations. This case study should be valuable for future reforms at
other institutions, both in the Gulf Region and developing world, facing
similar challenges involving SI of PER-based instruction outside the US.Comment: v1: 28 pages, 9 figures. v2: 19 pages, 6 figures, includes major
reorganization and revisions based on anonymous peer review. v3: 19 pages, 6
figures, minor revisions based on anonymous peer revie
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