22,341 research outputs found
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Most existing recommender systems represent a user's preference with a
feature vector, which is assumed to be fixed when predicting this user's
preferences for different items. However, the same vector cannot accurately
capture a user's varying preferences on all items, especially when considering
the diverse characteristics of various items. To tackle this problem, in this
paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to
model user diverse preferences for various items. In particular, for each
user-item pair, we propose an attention neural network, which exploits the
item's multimodal features to estimate the user's special attention to
different aspects of this item. The obtained attention is then integrated into
a metric-based learning method to predict the user preference on this item. The
advantage of metric learning is that it can naturally overcome the problem of
dot product similarity, which is adopted by matrix factorization (MF) based
recommendation models but does not satisfy the triangle inequality property. In
addition, it is worth mentioning that the attention mechanism cannot only help
model user's diverse preferences towards different items, but also overcome the
geometrically restrictive problem caused by collaborative metric learning.
Extensive experiments on large-scale real-world datasets show that our model
can substantially outperform the state-of-the-art baselines, demonstrating the
potential of modeling user diverse preference for recommendation.Comment: Accepted by ACM Multimedia 2019 as a full pape
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
Multi-behavioral sequential recommendation has recently attracted increasing
attention. However, existing methods suffer from two major limitations.
Firstly, user preferences and intents can be described in fine-grained detail
from multiple perspectives; yet, these methods fail to capture their
multi-aspect nature. Secondly, user behaviors may contain noises, and most
existing methods could not effectively deal with noises. In this paper, we
present an attentive recurrent model with multiple projections to capture
Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect
preferences from target behaviors, we propose a multi-aspect projection
mechanism for generating multiple preference representations from multiple
aspects. To extract multi-aspect intents from multi-typed behaviors, we propose
a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The
attention mechanism can filter out noises and generate multiple intent
representations from different aspects. To adaptively fuse user preferences and
intents, we propose a multi-aspect gated fusion mechanism. Extensive
experiments conducted on real-world datasets have demonstrated the
effectiveness of our model
Research on Personalized Learning Resource Recommendation Based on Knowledge Graph Technology
In the face of the dilemma of learners\u27 learning loss and information overload in information resources, a personalized learning resource recommendation algorithm is proposed by conducting in-depth and extensive research on the knowledge graph. This algorithm relies on the similarity or correlation between learners\u27 characteristics and course knowledge (learning resources) for recommendation. It analyzes learners\u27 characteristics in depth from four aspects: data collection and processing, model construction, resource and path recommendation, and model application, and establishes a multi layered dynamic feature model for learners; Analyze the core elements of the curriculum knowledge graph, decompose the curriculum knowledge into nanoscale knowledge granularity, and construct a curriculum knowledge graph model. The experimental results indicate that this algorithm improves learners\u27 learning efficiency and promotes their personalized development
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
The recent success of large language models (LLMs) has shown great potential
to develop more powerful conversational recommender systems (CRSs), which rely
on natural language conversations to satisfy user needs. In this paper, we
embark on an investigation into the utilization of ChatGPT for conversational
recommendation, revealing the inadequacy of the existing evaluation protocol.
It might over-emphasize the matching with the ground-truth items or utterances
generated by human annotators, while neglecting the interactive nature of being
a capable CRS. To overcome the limitation, we further propose an interactive
Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user
simulators. Our evaluation approach can simulate various interaction scenarios
between users and systems. Through the experiments on two publicly available
CRS datasets, we demonstrate notable improvements compared to the prevailing
evaluation protocol. Furthermore, we emphasize the evaluation of
explainability, and ChatGPT showcases persuasive explanation generation for its
recommendations. Our study contributes to a deeper comprehension of the
untapped potential of LLMs for CRSs and provides a more flexible and
easy-to-use evaluation framework for future research endeavors. The codes and
data are publicly available at https://github.com/RUCAIBox/iEvaLM-CRS.Comment: Accepted by EMNLP 202
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