Study of Thermal Characteristics and Emissions during Cold Start using an on-board Measuring Method for Modern SI Car Real World Urban Driving

2008 ◽  
Vol 1 (1) ◽  
pp. 804-819 ◽  
Author(s):  
Hu Li ◽  
Gordon E Andrews ◽  
Dimitrios Savvidis ◽  
Basil Daham ◽  
Karl Ropkins ◽  
...  
Keyword(s):  
Real World ◽  
Thermal Characteristics ◽  
Cold Start ◽  
Measuring Method

Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model

2016 ◽  
Vol 12 (2) ◽  
pp. 126-149 ◽  
Author(s):  
Masoud Mansoury ◽  
Mehdi Shajari
Keyword(s):  
Real World ◽  
Design Methodology ◽  
Cold Start ◽  
Selection Function ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
User Similarity ◽  
Active User ◽  
Similarity Model

Purpose This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users). Design/methodology/approach A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results. Findings Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items. Originality/value In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

scholarly journals Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhao Li ◽  
Haobo Wang ◽  
Donghui Ding ◽  
Shichang Hu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Contextual Information ◽  
Cold Start ◽  
Training Data ◽  
Learning To Learn ◽  
User Interests ◽  
Synthetic Datasets ◽  
Fresh Products ◽  
Embedding Performance ◽  
Deep Interest

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications.

scholarly journals Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Zhijie Qiu ◽  
Jun Rao ◽  
Yi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Small Subset ◽  
Multiple Channels ◽  
Attention Network

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

Diesel Cold Start into Congested Real World Traffic: Comparison of Diesel, B50, B100 for Gaseous Emissions

2013 ◽  
Author(s):  
Seyed Ali Hadavi ◽  
Hu Li ◽  
Gordon Andrews ◽  
Grzegorz przybyla ◽  
Buland Dizayi ◽  
...  
Keyword(s):  
Real World ◽  
Cold Start ◽  
Gaseous Emissions

A fleet-prediction oriented catalyst model for highly transient driving in cold and hot mode conditions

2012 ◽  
Vol 13 (5) ◽  
pp. 497-513 ◽  
Author(s):  
Martin Weilenmann ◽  
Dimitrios N Tsinoglou
Keyword(s):  
Real World ◽  
Catalytic Converter ◽  
Cold Start ◽  
Catalyst Design ◽  
Pollutant Emissions ◽  
Oxygen Storage ◽  
Catalytic Converters ◽  
Driving Cycles ◽  
Wide Range ◽  
Oxidation Catalytic Converter

Various models for simulating catalytic converters are given in the literature. They deal with a wide range of different aspects. In addition to the type of catalytic converter (three-way catalytic converter, diesel oxidation catalytic converter, etc.), the aspect of complexity versus accuracy and speed can be tackled using different approaches. Moreover, the desired use has an influence on the model structure: optimization of catalyst design or prediction of emissions from real-world traffic situations or optimization of air–fuel ratio control? The model described here has been developed to predict emissions in arbitrary real-world driving patterns, both for hot driving as well as for cold-start situations. As these tests mainly last over 30 minutes (real time), the calculation effort should be small. The model should be easy to parameterize, as it should be applicable to vehicles from traffic. A model with a reduced set of chemical reactions has been developed with a particular focus on the thermal balance for cold-start cycles. Its outputs are the pollutant emissions at the tailpipe if the emissions, exhaust mass flow and temperature from the engine are given. It is applied to three-way catalytic converters. It models the chemical phenomena almost entirely based on oxygen storage and release reactions, which dominate highly transient situations. The model has been validated against a large database of measured driving cycles, carried out using different types of cars. It presents an acceptable degree of correlation between simulated and experimental results.

scholarly journals SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

2017 ◽  
Author(s):  
Chenwei Cai ◽  
Ruining He ◽  
Julian McAuley
Keyword(s):  
Markov Chains ◽  
Social Relationships ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Cold Start ◽  
Additional Information ◽  
New Methods ◽  
Real World Datasets ◽  
Sequential Information

Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.

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