Cold start (automotive)
View article
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings Open
Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By a…
View article
Social Network and Tag Sources Based Augmenting Collaborative Recommender System Open
Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input d…
View article
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems Open
Collaborative filtering (CF) is a widely used approach in recommender systems to solve many real-world problems. Traditional CF-based methods employ the user-item matrix which encodes the individual preferences of users for items for learn…
View article
Contrastive Learning for Cold-Start Recommendation Open
Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative s…
View article
Review of thermal management of catalytic converters to decrease engine emissions during cold start and warm up Open
Catalytic converters mitigate carbon monoxide, hydrocarbon, nitrogen oxides and particulate matter emissions from internal combustion engines, and allow meeting the increasingly stringent emission regulations. However, catalytic converters…
View article
Impact of cold temperature on Euro 6 passenger car emissions Open
Hydrocarbons, CO, NOx, NH3, N2O, CO2 and particulate matter emissions affect air quality, global warming and human health. Transport sector is an important source of these pollutants and high pollution episodes are often experienced during…
View article
Social Collaborative Filtering by Trust Open
In order to sufficiently validate the performance of our proposed methods, we choose four representative data sets related to social collaborative filtering for our experiments, which are taken from popular social networking web sites includ…
View article
Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems Open
As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recom…
View article
Effect of cold start emissions from gasoline-fueled engines of light-duty vehicles at low and high ambient temperatures: Recent trends Open
The cold-start condition is an important factor affecting vehicle emissions from gasoline direct injection (GDI) and port fuel injection (PFI) vehicles. This paper studied the recent trends in cold-start emissive behavior with the impact o…
View article
On-road measurement of NH 3 emissions from gasoline and diesel passenger cars during real world driving conditions Open
NH3 is a precursor of PM2.5 which deteriorates urban air quality, affects human health and impacts the global radiation budget. Since vehicles are important sources of NH3 in urban areas, we have satisfactorily studied the possibility of m…
View article
A Comprehensive Review of Solutions and Strategies for Cold Start of Automotive Proton Exchange Membrane Fuel Cells Open
Proton exchange membrane fuel cell (PEMFC) can be a significant eco-friendly alternative power source for vehicles. However, under subfreezing conditions, cell degradation and irreversible performance decay can occur because of ice formati…
View article
A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering Open
Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous syste…
View article
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks Open
Recently, embedding techniques have achieved impressive success in\nrecommender systems. However, the embedding techniques are data demanding and\nsuffer from the cold-start problem. Especially, for the cold-start item which\nonly has limi…
View article
A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering Open
Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot di…
View article
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences Open
Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fundamentally differen…
View article
Multimodal Movie Recommendation System Using Deep Learning Open
Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed exte…
View article
MetaKG: Meta-Learning on Knowledge Graph for Cold-Start Recommendation Open
A knowledge graph (KG) consists of a set of interconnected typed entities and their attributes. Recently, KGs are popularly used as the auxiliary information to enable more accurate, explainable, and diverse user preference recommendations…
View article
Regulating particle number measurements from the tailpipe of light-duty vehicles: The next step? Open
Light-duty vehicle emission regulation in the European Union requires the dilution of the whole exhaust in a dilution tunnel with constant volume sampling prior to emission measurements. This methodology avoids measurement uncertainties as…
View article
A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach Open
In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to…
View article
Task-adaptive Neural Process for User Cold-Start Recommendation Open
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studies seek to address this challenge from the perspective of me…
View article
Movie genome: alleviating new item cold start in movie recommendation Open
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services,…
View article
Metadata Embeddings for User and Item Cold-start Recommendations Open
I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interact…
View article
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors Open
Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes…
View article
Low-Rank Linear Cold-Start Recommendation from Social Data Open
The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative filtering algorithms that inherently rely on su…
View article
Fairness among New Items in Cold Start Recommender Systems Open
This paper investigates recommendation fairness among new items. While previous efforts have studied fairness in recommender systems and shown success in improving fairness, they mainly focus on scenarios where unfairness arises due to bia…
View article
A Fast Recommender System for Cold User Using Categorized Items Open
In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active…
View article
A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation Open
As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending a…
View article
Content-Aware Collaborative Music Recommendation Using Pre-Trained Neural Networks. Open
[TODO] Add abstract here.
View article
Comparison of regulated emission factors of Euro 6 LDV in Nordic temperatures and cold start conditions: Diesel- and gasoline direct-injection Open
Local air pollution in Norwegian wintertime is characterized by an increase in NO2 concentrations, due to poor dispersion and increased vehicle emissions. The focus of this study is therefore the characterisation of exhaust vehicle emissio…
View article
Contrastive Collaborative Filtering for Cold-Start Item Recommendation Open
The cold-start problem is a long-standing challenge in recommender systems.\nAs a promising solution, content-based generative models usually project a\ncold-start item's content onto a warm-start item embedding to capture\ncollaborative s…