6,228 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
Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function
Task-oriented dialog systems enable users to accomplish tasks using natural
language. State-of-the-art systems respond to users in the same way regardless
of their personalities, although personalizing dialogues can lead to higher
levels of adoption and better user experiences. Building personalized dialog
systems is an important, yet challenging endeavor and only a handful of works
took on the challenge. Most existing works rely on supervised learning
approaches and require laborious and expensive labeled training data for each
user profile. Additionally, collecting and labeling data for each user profile
is virtually impossible. In this work, we propose a novel framework, P-ToD, to
personalize task-oriented dialog systems capable of adapting to a wide range of
user profiles in an unsupervised fashion using a zero-shot generalizable reward
function. P-ToD uses a pre-trained GPT-2 as a backbone model and works in three
phases. Phase one performs task-specific training. Phase two kicks off
unsupervised personalization by leveraging the proximal policy optimization
algorithm that performs policy gradients guided by the zero-shot generalizable
reward function. Our novel reward function can quantify the quality of the
generated responses even for unseen profiles. The optional final phase
fine-tunes the personalized model using a few labeled training examples. We
conduct extensive experimental analysis using the personalized bAbI dialogue
benchmark for five tasks and up to 180 diverse user profiles. The experimental
results demonstrate that P-ToD, even when it had access to zero labeled
examples, outperforms state-of-the-art supervised personalization models and
achieves competitive performance on BLEU and ROUGE metrics when compared to a
strong fully-supervised GPT-2 baselineComment: 11 pages, 4 tables, 31st ACM International Conference on Information
and Knowledge Management (CIKM'22
Reinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics.
Reinforcement learning is a framework for decision-making problems in which an
agent interacts through trial-and-error with its environment to discover an
optimal behavior. Since interaction is a key component in both reinforcement
learning and social robotics, it can be a well-suited approach for real-world
interactions with physically embodied social robots. The scope of the paper is
focused particularly on studies that include social physical robots and
real-world human-robot interactions with users. We present a thorough analysis
of reinforcement learning approaches in social robotics. In addition to a
survey, we categorize existent reinforcement learning approaches based on the
used method and the design of the reward mechanisms. Moreover, since
communication capability is a prominent feature of social robots, we discuss
and group the papers based on the communication medium used for reward
formulation. Considering the importance of designing the reward function, we
also provide a categorization of the papers based on the nature of the reward.
This categorization includes three major themes: interactive reinforcement
learning, intrinsically motivated methods, and task performance-driven methods.
The benefits and challenges of reinforcement learning in social robotics,
evaluation methods of the papers regarding whether or not they use subjective
and algorithmic measures, a discussion in the view of real-world reinforcement
learning challenges and proposed solutions, the points that remain to be
explored, including the approaches that have thus far received less attention
is also given in the paper. Thus, this paper aims to become a starting point
for researchers interested in using and applying reinforcement learning methods
in this particular research field
A Conditional Generative Chatbot using Transformer Model
A Chatbot serves as a communication tool between a human user and a machine
to achieve an appropriate answer based on the human input. In more recent
approaches, a combination of Natural Language Processing and sequential models
are used to build a generative Chatbot. The main challenge of these models is
their sequential nature, which leads to less accurate results. To tackle this
challenge, in this paper, a novel end-to-end architecture is proposed using
conditional Wasserstein Generative Adversarial Networks and a transformer model
for answer generation in Chatbots. While the generator of the proposed model
consists of a full transformer model to generate an answer, the discriminator
includes only the encoder part of a transformer model followed by a classifier.
To the best of our knowledge, this is the first time that a generative Chatbot
is proposed using the embedded transformer in both generator and discriminator
models. Relying on the parallel computing of the transformer model, the results
of the proposed model on the Cornell Movie-Dialog corpus and the Chit-Chat
datasets confirm the superiority of the proposed model compared to
state-of-the-art alternatives using different evaluation metrics
Contextual Understanding in Neural Dialog Systems: the Integration of External Knowledge Graphs for Generating Coherent and Knowledge-rich Conversations
The integration of external knowledge graphs has emerged as a powerful approach to enrich conversational AI systems with coherent and knowledge-rich conversations. This paper provides an overview of the integration process and highlights its benefits. Knowledge graphs serve as structured representations of information, capturing the relationships between entities through nodes and edges. They offer an organized and efficient means of representing factual knowledge. External knowledge graphs, such as DBpedia, Wikidata, Freebase, and Google's Knowledge Graph, are pre-existing repositories that encompass a wide range of information across various domains. These knowledge graphs are compiled by aggregating data from diverse sources, including online encyclopedias, databases, and structured repositories. To integrate an external knowledge graph into a conversational AI system, a connection needs to be established between the system and the knowledge graph. This can be achieved through APIs or by importing a copy of the knowledge graph into the AI system's internal storage. Once integrated, the conversational AI system can query the knowledge graph to retrieve relevant information when a user poses a question or makes a statement. When analyzing user inputs, the conversational AI system identifies entities or concepts that require additional knowledge. It then formulates queries to retrieve relevant information from the integrated knowledge graph. These queries may involve searching for specific entities, retrieving related entities, or accessing properties and attributes associated with the entities. The obtained information is used to generate coherent and knowledge-rich responses. By integrating external knowledge graphs, conversational AI systems can augment their internal knowledge base and provide more accurate and up-to-date responses. The retrieved information allows the system to extract relevant facts, provide detailed explanations, or offer additional context. This integration empowers AI systems to deliver comprehensive and insightful responses that enhance user experience. As external knowledge graphs are regularly updated with new information and improvements, conversational AI systems should ensure their integrated knowledge graphs remain current. This can be achieved through periodic updates, either by synchronizing the system's internal representation with the external knowledge graph or by querying the external knowledge graph in real-time
Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)
1st Doctoral Consortium at the European Conference on
Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020
Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option
Designing Human-Centered Collective Intelligence
Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence
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