223 research outputs found
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two
different fields of artificial intelligence: neurosymbolic and statistical
relational artificial intelligence. Neurosymbolic artificial intelligence
(NeSy) studies the integration of symbolic reasoning and neural networks, while
statistical relational artificial intelligence (StarAI) focuses on integrating
logic with probabilistic graphical models. This survey identifies seven shared
dimensions between these two subfields of AI. These dimensions can be used to
characterize different NeSy and StarAI systems. They are concerned with (1) the
approach to logical inference, whether model or proof-based; (2) the syntax of
the used logical theories; (3) the logical semantics of the systems and their
extensions to facilitate learning; (4) the scope of learning, encompassing
either parameter or structure learning; (5) the presence of symbolic and
subsymbolic representations; (6) the degree to which systems capture the
original logic, probabilistic, and neural paradigms; and (7) the classes of
learning tasks the systems are applied to. By positioning various NeSy and
StarAI systems along these dimensions and pointing out similarities and
differences between them, this survey contributes fundamental concepts for
understanding the integration of learning and reasoning.Comment: To appear in Artificial Intelligence. Shorter version at IJCAI 2020
survey track, https://www.ijcai.org/proceedings/2020/0688.pd
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Embedding based Knowledge Graph (KG) Completion has gained much attention
over the past few years. Most of the current algorithms consider a KG as a
multidirectional labeled graph and lack the ability to capture the semantics
underlying the schematic information. In a separate development, a vast amount
of information has been captured within the Large Language Models (LLMs) which
has revolutionized the field of Artificial Intelligence. KGs could benefit from
these LLMs and vice versa. This vision paper discusses the existing algorithms
for KG completion based on the variations for generating KG embeddings. It
starts with discussing various KG completion algorithms such as transductive
and inductive link prediction and entity type prediction algorithms. It then
moves on to the algorithms utilizing type information within the KGs, LLMs, and
finally to algorithms capturing the semantics represented in different
description logic axioms. We conclude the paper with a critical reflection on
the current state of work in the community and give recommendations for future
directions
Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective
Algorithmic rankers have a profound impact on our increasingly data-driven
society. From leisurely activities like the movies that we watch, the
restaurants that we patronize; to highly consequential decisions, like making
educational and occupational choices or getting hired by companies -- these are
all driven by sophisticated yet mostly inaccessible rankers. A small change to
how these algorithms process the rankees (i.e., the data items that are ranked)
can have profound consequences. For example, a change in rankings can lead to
deterioration of the prestige of a university or have drastic consequences on a
job candidate who missed out being in the list of the preferred top-k for an
organization. This paper is a call to action to the human-centered data science
research community to develop principled methods, measures, and metrics for
studying the interactions among the socio-technical context of use,
technological innovations, and the resulting consequences of algorithmic
rankings on multiple stakeholders. Given the spate of new legislations on
algorithmic accountability, it is imperative that researchers from social
science, human-computer interaction, and data science work in unison for
demystifying how rankings are produced, who has agency to change them, and what
metrics of socio-technical impact one must use for informing the context of
use.Comment: Accepted for Interrogating Human-Centered Data Science workshop at
CHI'2
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
As the focus on Large Language Models (LLMs) in the field of recommendation
intensifies, the optimization of LLMs for recommendation purposes (referred to
as LLM4Rec) assumes a crucial role in augmenting their effectiveness in
providing recommendations. However, existing approaches for LLM4Rec often
assess performance using restricted sets of candidates, which may not
accurately reflect the models' overall ranking capabilities. In this paper, our
objective is to investigate the comprehensive ranking capacity of LLMs and
propose a two-step grounding framework known as BIGRec (Bi-step Grounding
Paradigm for Recommendation). It initially grounds LLMs to the recommendation
space by fine-tuning them to generate meaningful tokens for items and
subsequently identifies appropriate actual items that correspond to the
generated tokens. By conducting extensive experiments on two datasets, we
substantiate the superior performance, capacity for handling few-shot
scenarios, and versatility across multiple domains exhibited by BIGRec.
Furthermore, we observe that the marginal benefits derived from increasing the
quantity of training samples are modest for BIGRec, implying that LLMs possess
the limited capability to assimilate statistical information, such as
popularity and collaborative filtering, due to their robust semantic priors.
These findings also underline the efficacy of integrating diverse statistical
information into the LLM4Rec framework, thereby pointing towards a potential
avenue for future research. Our code and data are available at
https://github.com/SAI990323/Grounding4Rec.Comment: 17 page
Knowledge Prompt-tuning for Sequential Recommendation
Pre-trained language models (PLMs) have demonstrated strong performance in
sequential recommendation (SR), which are utilized to extract general
knowledge. However, existing methods still lack domain knowledge and struggle
to capture users' fine-grained preferences. Meanwhile, many traditional SR
methods improve this issue by integrating side information while suffering from
information loss. To summarize, we believe that a good recommendation system
should utilize both general and domain knowledge simultaneously. Therefore, we
introduce an external knowledge base and propose Knowledge Prompt-tuning for
Sequential Recommendation (\textbf{KP4SR}). Specifically, we construct a set of
relationship templates and transform a structured knowledge graph (KG) into
knowledge prompts to solve the problem of the semantic gap. However, knowledge
prompts disrupt the original data structure and introduce a significant amount
of noise. We further construct a knowledge tree and propose a knowledge tree
mask, which restores the data structure in a mask matrix form, thus mitigating
the noise problem. We evaluate KP4SR on three real-world datasets, and
experimental results show that our approach outperforms state-of-the-art
methods on multiple evaluation metrics. Specifically, compared with PLM-based
methods, our method improves NDCG@5 and HR@5 by \textcolor{red}{40.65\%} and
\textcolor{red}{36.42\%} on the books dataset, \textcolor{red}{11.17\%} and
\textcolor{red}{11.47\%} on the music dataset, and \textcolor{red}{22.17\%} and
\textcolor{red}{19.14\%} on the movies dataset, respectively. Our code is
publicly available at the link:
\href{https://github.com/zhaijianyang/KP4SR}{\textcolor{blue}{https://github.com/zhaijianyang/KP4SR}.
A Landscape of First-Order Linear Temporal Logics in Infinite-State Verification and Temporal Ontologies
We provide an overview of the main attempts to formalize and reason about the evolution over time of complex domains, through the lens of first-order temporal logics. Different communities have studied similar problems for decades, and some unification of concepts, problems and formalisms is a much needed but not simple task
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling
Personalized chatbots focus on endowing the chatbots with a consistent
personality to behave like real users and further act as personal assistants.
Previous studies have explored generating implicit user profiles from the
user's dialogue history for building personalized chatbots. However, these
studies only use the response generation loss to train the entire model, thus
it is prone to suffer from the problem of data sparsity. Besides, they
overemphasize the final generated response's quality while ignoring the
correlations and fusions between the user's dialogue history, leading to rough
data representations and performance degradation. To tackle these problems, we
propose a self-supervised learning framework MCP for capturing better
representations from users' dialogue history for personalized chatbots.
Specifically, we apply contrastive sampling methods to leverage the supervised
signals hidden in user dialog history, and generate the pre-training samples
for enhancing the model. We design three pre-training tasks based on three
types of contrastive pairs from user dialogue history, namely response pairs,
sequence augmentation pairs, and user pairs. We pre-train the utterance encoder
and the history encoder towards the contrastive objectives and use these
pre-trained encoders for generating user profiles while personalized response
generation. Experimental results on two real-world datasets show a significant
improvement in our proposed model MCP compared with the existing methods
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
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