1,524 research outputs found
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
Advanced recommender systems usually involve multiple domains (scenarios or
categories) for various marketing strategies, and users interact with them to
satisfy their diverse demands. The goal of multi-domain recommendation is to
improve the recommendation performance of all domains simultaneously.
Conventional graph neural network based methods usually deal with each domain
separately, or train a shared model for serving all domains. The former fails
to leverage users' cross-domain behaviors, making the behavior sparseness issue
a great obstacle. The latter learns shared user representation with respect to
all domains, which neglects users' domain-specific preferences. These
shortcomings greatly limit their performance in multi-domain recommendation.
To tackle the limitations, an appropriate way is to learn from multi-domain
user feedbacks and obtain separate user representations to characterize their
domain-specific preferences. In this paper we propose , a
hierarchical hypergraph network based correlative preference transfer framework
for multi-domain recommendation. represents multi-domain
feedbacks into a unified graph to help preference transfer via taking full
advantage of users' multi-domain behaviors. We incorporate two hyperedge-based
modules, namely dynamic item transfer module (Hyper-I) and adaptive user
aggregation module (Hyper-U). Hyper-I extracts correlative information from
multi-domain user-item feedbacks for eliminating domain discrepancy of item
representations. Hyper-U aggregates users' scattered preferences in multiple
domains and further exploits the high-order (not only pair-wise) connections
among them to learn user representations. Experimental results on both public
datasets and large-scale production datasets verify the superiority of
for multi-domain recommendation.Comment: Work in progres
A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES
As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms
A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES
As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms
MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS
The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,
Distributed Primal-Dual Optimization for Online Multi-Task Learning
Conventional online multi-task learning algorithms suffer from two critical
limitations: 1) Heavy communication caused by delivering high velocity of
sequential data to a central machine; 2) Expensive runtime complexity for
building task relatedness. To address these issues, in this paper we consider a
setting where multiple tasks are geographically located in different places,
where one task can synchronize data with others to leverage knowledge of
related tasks. Specifically, we propose an adaptive primal-dual algorithm,
which not only captures task-specific noise in adversarial learning but also
carries out a projection-free update with runtime efficiency. Moreover, our
model is well-suited to decentralized periodic-connected tasks as it allows the
energy-starved or bandwidth-constraint tasks to postpone the update.
Theoretical results demonstrate the convergence guarantee of our distributed
algorithm with an optimal regret. Empirical results confirm that the proposed
model is highly effective on various real-world datasets
How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed
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