913 research outputs found
Competing orders and inter-layer tunnelling in cuprate superconductors: A finite temperature Landau theory
We propose a finite temperature Landau theory that describes competing orders
and interlayer tunneling in cuprate superconductors as an important extension
to a corresponding theory at zero temperature [Nature {\bf 428}, 53 (2004)],
where the superconducting transition temperature is defined in three
possible ways as a function of the zero temperature order parameter. For given
parameters, our theory determines without any ambiguity. In mono- and
double-layer systems we discuss the relation between zero temperature order
parameter and the associated transition temperature in the presence of
competing orders, and draw a connection to the puzzling experimental fact that
the pseudo-gap temperature is much higher than the corresponding energy scale
near optimum doping. Applying the theory to multi-layer systems, we calculate
the layer-number dependence of . In a reasonable parameter space the
result turns out to be in agreement with experiments.Comment: 5 pages, 3 figure
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Online recommender systems (RS) aim to match user needs with the vast amount
of resources available on various platforms. A key challenge is to model user
preferences accurately under the condition of data sparsity. To address this
challenge, some methods have leveraged external user behavior data from
multiple platforms to enrich user representation. However, all of these methods
require a consistent user ID across platforms and ignore the information from
similar users. In this study, we propose RUEL, a novel retrieval-based
sequential recommender that can effectively incorporate external anonymous user
behavior data from Edge browser logs to enhance recommendation. We first
collect and preprocess a large volume of Edge browser logs over a one-year
period and link them to target entities that correspond to candidate items in
recommendation datasets. We then design a contrastive learning framework with a
momentum encoder and a memory bank to retrieve the most relevant and diverse
browsing sequences from the full browsing log based on the semantic similarity
between user representations. After retrieval, we apply an item-level attentive
selector to filter out noisy items and generate refined sequence embeddings for
the final predictor. RUEL is the first method that connects user browsing data
with typical recommendation datasets and can be generalized to various
recommendation scenarios and datasets. We conduct extensive experiments on four
real datasets for sequential recommendation tasks and demonstrate that RUEL
significantly outperforms state-of-the-art baselines. We also conduct ablation
studies and qualitative analysis to validate the effectiveness of each
component of RUEL and provide additional insights into our method.Comment: CIKM 2023 AD
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Recent multilingual pre-trained models have shown better performance in
various multilingual tasks. However, these models perform poorly on
multilingual retrieval tasks due to lacking multilingual training data. In this
paper, we propose to mine and generate self-supervised training data based on a
large-scale unlabeled corpus. We carefully design a mining method which
combines the sparse and dense models to mine the relevance of unlabeled queries
and passages. And we introduce a query generator to generate more queries in
target languages for unlabeled passages. Through extensive experiments on Mr.
TYDI dataset and an industrial dataset from a commercial search engine, we
demonstrate that our method performs better than baselines based on various
pre-trained multilingual models. Our method even achieves on-par performance
with the supervised method on the latter dataset.Comment: EMNLP 2022 Finding
When Social Influence Meets Item Inference
Research issues and data mining techniques for product recommendation and
viral marketing have been widely studied. Existing works on seed selection in
social networks do not take into account the effect of product recommendations
in e-commerce stores. In this paper, we investigate the seed selection problem
for viral marketing that considers both effects of social influence and item
inference (for product recommendation). We develop a new model, Social Item
Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we
formulate a seed selection problem, called Social Item Maximization Problem
(SIMP), and prove the hardness of SIMP. We design an efficient algorithm with
performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and
develop a new index structure, called SIG-index, to accelerate the computation
of diffusion process in HAG. Moreover, to construct realistic SIG models for
SIMP, we develop a statistical inference based framework to learn the weights
of hyperedges from data. Finally, we perform a comprehensive evaluation on our
proposals with various baselines. Experimental result validates our ideas and
demonstrates the effectiveness and efficiency of the proposed model and
algorithms over baselines.Comment: 12 page
Reinforced Multi-Teacher Selection for Knowledge Distillation
In natural language processing (NLP) tasks, slow inference speed and huge
footprints in GPU usage remain the bottleneck of applying pre-trained deep
models in production. As a popular method for model compression, knowledge
distillation transfers knowledge from one or multiple large (teacher) models to
a small (student) model. When multiple teacher models are available in
distillation, the state-of-the-art methods assign a fixed weight to a teacher
model in the whole distillation. Furthermore, most of the existing methods
allocate an equal weight to every teacher model. In this paper, we observe
that, due to the complexity of training examples and the differences in student
model capability, learning differentially from teacher models can lead to
better performance of student models distilled. We systematically develop a
reinforced method to dynamically assign weights to teacher models for different
training instances and optimize the performance of student model. Our extensive
experimental results on several NLP tasks clearly verify the feasibility and
effectiveness of our approach.Comment: AAAI 202
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
The Differentiable Search Index (DSI) is an emerging paradigm for information
retrieval. Unlike traditional retrieval architectures where index and retrieval
are two different and separate components, DSI uses a single transformer model
to perform both indexing and retrieval.
In this paper, we identify and tackle an important issue of current DSI
models: the data distribution mismatch that occurs between the DSI indexing and
retrieval processes. Specifically, we argue that, at indexing, current DSI
methods learn to build connections between the text of long documents and the
identifier of the documents, but then retrieval of document identifiers is
based on queries that are commonly much shorter than the indexed documents.
This problem is further exacerbated when using DSI for cross-lingual retrieval,
where document text and query text are in different languages.
To address this fundamental problem of current DSI models, we propose a
simple yet effective indexing framework for DSI, called DSI-QG. When indexing,
DSI-QG represents documents with a number of potentially relevant queries
generated by a query generation model and re-ranked and filtered by a
cross-encoder ranker. The presence of these queries at indexing allows the DSI
models to connect a document identifier to a set of queries, hence mitigating
data distribution mismatches present between the indexing and the retrieval
phases. Empirical results on popular mono-lingual and cross-lingual passage
retrieval datasets show that DSI-QG significantly outperforms the original DSI
model.Comment: 11 page
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