851 research outputs found
Learning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborative
filtering (CF) based approaches with shallow or deep models -- usually work
with various unstructured information sources for recommendation, such as
textual reviews, visual images, and various implicit or explicit feedbacks.
Though structured knowledge bases were considered in content-based approaches,
they have been largely neglected recently due to the availability of vast
amount of data, and the learning power of many complex models.
However, structured knowledge bases exhibit unique advantages in personalized
recommendation systems. When the explicit knowledge about users and items is
considered for recommendation, the system could provide highly customized
recommendations based on users' historical behaviors. A great challenge for
using knowledge bases for recommendation is how to integrated large-scale
structured and unstructured data, while taking advantage of collaborative
filtering for highly accurate performance. Recent achievements on knowledge
base embedding sheds light on this problem, which makes it possible to learn
user and item representations while preserving the structure of their
relationship with external knowledge. In this work, we propose to reason over
knowledge base embeddings for personalized recommendation. Specifically, we
propose a knowledge base representation learning approach to embed
heterogeneous entities for recommendation. Experimental results on real-world
dataset verified the superior performance of our approach compared with
state-of-the-art baselines
BERT with History Answer Embedding for Conversational Question Answering
Conversational search is an emerging topic in the information retrieval
community. One of the major challenges to multi-turn conversational search is
to model the conversation history to answer the current question. Existing
methods either prepend history turns to the current question or use complicated
attention mechanisms to model the history. We propose a conceptually simple yet
highly effective approach referred to as history answer embedding. It enables
seamless integration of conversation history into a conversational question
answering (ConvQA) model built on BERT (Bidirectional Encoder Representations
from Transformers). We first explain our view that ConvQA is a simplified but
concrete setting of conversational search, and then we provide a general
framework to solve ConvQA. We further demonstrate the effectiveness of our
approach under this framework. Finally, we analyze the impact of different
numbers of history turns under different settings to provide new insights into
conversation history modeling in ConvQA.Comment: Accepted to SIGIR 2019 as a short pape
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Structural dynamic reliability evaluation under consideration of fuzzy strength and fuzzy stress
A new dynamic reliability analysis under repeated or multiple series fuzzy loads and fuzzy strength is proposed in this paper. The proposed prediction models of structural dynamic fuzzy reliability with and without strength degeneration are established by using fuzzy theory and stress-strength interference theory. The fuzzy reliability is converted to probability reliability. The results have shown that the proposed model is feasible and practicable
Universal steganography model for low bit-rate speech codec
Low bit-rate speech codec offers so many advantages over other codecs that it has become increasingly popular in audio communications such as mobile and VoIP (Voice over Internet Protocol) communications, and thus researching steganography in low bit-rate speech codec is of important significance. In this study, we proposed a universal VoIP steganography model for low bit-rate speech codec that uses the PESQ deterioration rate and the decoding error to automatically choose a data embedding algorithm for each VoIP bitstream, which enables ones to achieve covert communications using a low bit-rate speech codec efficiently and securely. Since no or little attention has been paid to steganography in iSAC (Internet Speech Audio Codec), it was chosen as the test codec to verify the effectiveness, security, and practicability of the proposed steganography model. The experimental results show that, with the proposed steganography model, it achieved the average PESQ deterioration rate of 4.04% (less than 5%, indicating strong imperceptibility) and a high data hiding capacity up to 12 bits/frame (400 bits/second, three times larger than other methods), and the proposed steganography model could effectively resist the latest steganalysis
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Recent years have witnessed the wide adoption of large language models (LLM)
in different fields, especially natural language processing and computer
vision. Such a trend can also be observed in recommender systems (RS). However,
most of related work treat LLM as a component of the conventional
recommendation pipeline (e.g., as a feature extractor) which may not be able to
fully leverage the generative power of LLM. Instead of separating the
recommendation process into multiple stages such as score computation and
re-ranking, this process can be simplified to one stage with LLM: directly
generating recommendations from the complete pool of items. This survey reviews
the progress, methods and future directions of LLM-based generative
recommendation by examining three questions: 1) What generative recommendation
is, 2) Why RS should advance to generative recommendation, and 3) How to
implement LLM-based generative recommendation for various RS tasks. We hope
that the survey can provide the context and guidance needed to explore this
interesting and emerging topic
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
In this work, we introduce the concept of complex text style transfer tasks,
and constructed complex text datasets based on two widely applicable scenarios.
Our dataset is the first large-scale data set of its kind, with 700 rephrased
sentences and 1,000 sentences from the game Genshin Impact. While large
language models (LLM) have shown promise in complex text style transfer, they
have drawbacks such as data privacy concerns, network instability, and high
deployment costs. To address these issues, we explore the effectiveness of
small models (less than T5-3B) with implicit style pre-training through
contrastive learning. We also propose a method for automated evaluation of text
generation quality based on alignment with human evaluations using ChatGPT.
Finally, we compare our approach with existing methods and show that our model
achieves state-of-art performances of few-shot text style transfer models
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