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Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tensor recommender systems. The proposed method utilizes the structure of a tensor response to integrate information from multiple modes, and creates an additional layer of nested latent factors to accommodate between-subjects dependency. One major advantage is that the proposed method is able to address the “cold-start” issue in the absence of information from new customers, new products or new contexts. Specifically, it provides more effective recommendations through sub-group information. To achieve scalable computation, we develop a new algorithm for the proposed method, which incorporates a maximum block improvement strategy into the cyclic blockwise-coordinate-descent algorithm. In theory, we investigate algorithmic properties for convergence from an arbitrary initial point and local convergence, along with the asymptotic consistency of estimated parameters. Finally, the proposed method is applied in simulations and IRI marketing data with 116 million observations of product sales. Numerical studies demonstrate that the proposed method outperforms existing competitors in the literature.

Cold Start problems in recommender systems pose various challenges in the adoption and use of recommender systems, especially for new item uptake and new user engagement. This restricts organizations to realize the business value of recommender systems as they have to incur marketing and operations costs to engage new users and promote new items. Owing to this, several studies have been done by recommender systems researchers to address the cold start problems. However, there has been very limited recent research done on collating these approaches and algorithms. To address this gap, the paper conducts a systematic literature review of various strategies and approaches proposed by researchers in the last decade, from January 2010 to December 2021, and synthesizes the same into two categories: data-driven strategies and approach-driven strategies. Furthermore, the approach-driven strategies are categorized into five main clusters based on deep learning, matrix factorization, hybrid approaches, or other novel approaches in collaborative filtering and content-based algorithms. The scope of this study is limited to a systematic literature review and it does not include an experimental study to benchmark and recommend the best approaches and their context of use in cold start scenarios.

Research on the problem of new user cold-start recommendation generally leverages user side information to suggest items to new users. This approach, however, is impractical due to privacy concerns. In this paper, we propose ColdGAN, an end-to-end GAN-based recommendation system that makes no use of side information to resolve the new user cold-start recommendation problem. The proposed ColdGAN explores the merit of GAN that enables precise data generation given imprecise data. Our generative network learns to predict item ratings that cold-start users would make in the future given their limited rating behavior data. The predicted ratings are evaluated by the discriminative network trained for determining whether the ratings are precise enough. Moreover, a novel rejuvenation function and relevant item loss are incorporated into ColdGAN to enhance the predictions made by the learned generative network. Experiments based on three real-world datasets demonstrate that ColdGAN significantly outperforms many state-of-the-art recommendation systems. Also, our designed rejuvenation function and relevant item loss are effective in guiding our generative network to infer item ratings of cold-start new users.

To break through the bottleneck of cold start technology of fuel cell vehicles (FCVs) in extreme environments, this paper conducts a working condition test on a certain type of fuel cell passenger vehicle, and monitors the energy flow dynamics in real time through a high-precision data acquisition system, and the system verification shows that the power of the stack exceeds 10kW within 100 seconds, and the thermal management system effectively breaks the icing, realizing the rapid activation of “low-temperature icing state-stable power generation”. The low-speed section successfully coped with the start-stop fluctuation, the medium-speed section maintained a stable output of 20-50kW, and the high-speed section reached 70kW transient peak power, verifying the reliability of hydrothermal management and hydrogen synergy of the stack. The fuel cell is the core power source, and the power battery realizes dynamic compensation through “peak shaving and valley filling”, and the power matching error of the system under all working conditions is controllable. This study confirms that the comprehensive performance of FCV at extremely low temperature meets the requirements of the national standard, and provides key technical support for the commercialization of severe cold areas.

State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart. This model leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. The deep content feature extractor can either directly predict the item embedding, or serve as a regularization prior, yielding two variants (strict and relaxed) of our model. Experimental results show that the proposed method reaches state-of-the-art results for both warm- and cold-start music recommendation tasks. We notably observe that exploiting deep neural networks for learning refined user/item interactions outperforms approaches using a more simple interaction model in a content-aware framework.

Recommender systems are one of the information filtering tools which can be employed to find interest items of users. Collaborative filtering is one of the recommendation methods to provide suggestions for target users based on the ratings of like-interest users. This method suffers from some shortcomings such as cold start problem leading to reduce the performance of recommender system in predicting unseen items. In this paper, we propose a hybrid recommendation method based on profile expansion technique to alleviate cold start problem in recommender systems. For this purpose, we take into consideration user’s demographic data (e.g. age, gender, and occupation) beside user’s rating data in order to enrich the neighborhood set of users. Specifically, two different strategies are used to enrich the rating profile of users by adding some additional ratings to them. The proposed rating profile expansion mechanism has a significant effect on the performance improvement of recommender systems especially when they are facing with cold start problem. The reason behind this claim is that the proposed mechanism makes a denser user-item rating matrix than the original one by adding some additional ratings to it. Obviously, providing a rating profile with further ratings for the target user leads to alleviate cold start problem in recommender systems. The expanded rating profiles are used to calculate similarity values between users and predict unseen items. The results of experiments demonstrate that the proposed method can achieve better performance than the other recommendation methods in terms of accuracy and rate coverage measures.

Providing relevant personalized recommendations for new users is one of the major challenges in recommender systems. This problem, known as the user cold start has been approached from different perspectives. In particular, cross-domain recommendation methods exploit data from source domains to address the lack of user preferences in a target domain. Most of the cross-domain approaches proposed so far follow the paradigm of collaborative filtering, and avoid analyzing the contents of the items, which are usually highly heterogeneous in the cross-domain setting. Content-based filtering, however, has been successfully applied in domains where item content and metadata play a key role. Such domains are not limited to scenarios where items do have text contents (e.g., books, news articles, scientific papers, and web pages), and where text mining and information retrieval techniques are often used. Potential application domains include those where items have associated metadata, e.g., genres, directors and actors for movies, and music styles, composers and themes for songs. With the advent of the Semantic Web, and its reference implementation Linked Data, a plethora of structured, interlinked metadata is available on the Web. These metadata represent a potential source of information to be exploited by content-based and hybrid filtering approaches. Motivated by the use of Linked Data for recommendation purposes, in this paper we present and evaluate a number of matrix factorization models for cross-domain collaborative filtering that leverage metadata as a bridge between items liked by users in different domains. We show that in case the underlying knowledge graph connects items from different domains and then in situations that benefit from cross-domain information, our models can provide better recommendations to new users while keeping a good trade-off between recommendation accuracy and diversity.

Measurement of car engines exhaust pollutants emissions is very important because of their harmful effects on the environment. This article presents the assessment of repeatability of the passenger car engine exhaust pollutants emission research results obtained in the conditions of a chassis dynamometer. The research was conducted in a climate chamber, enabling the temperature conditions to be determined from − 20 to + 30 °C. The emission of CO, CH4, CO2, NOX, THC, and NMHC was subjected to the analysis. The aim of the research is to draw attention to the accuracy of the pollutant emission research results in driving cycles, and the comparison of pollutant emission results and their repeatability obtained in successive NEDC cycles under cold and hot start conditions. The results of the analysis show that, in the case of a small number of measurements, the results repeatability analysis is necessary for a proper interpretation of the pollutant emission results on the basis of the mean value. According to the authors’ judgment, it is beneficial to determine the coefficient of variation for a more complete assessment of exhaust emission result repeatability obtained from a small number of measurements. This parameter is rarely presented by the authors of papers on exhaust components emission research.

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.

During cold start of vehicles with gasoline combustion engines, conversion of pollutants in the exhaust gas to inert products is very low due to low catalyst temperature. Only above the light-off temperature, significant conversion can be achieved. Previous strategies to reduce cold-start emissions have been focused on developing catalysts with a low light-off temperature. Electric catalyst heating systems have also been discussed repeatedly. A disadvantage of such systems is the required volume flow through the catalyst, which is necessary for heat transfer to the catalyst. In contrast, microwave-assisted heating allows direct introduction of thermal power into the catalyst due to dielectric losses of the catalyst materials. This work analyses simulation-based the influence of the material on the heatability by microwaves. The focus is on the substrate materials rather than the catalytically active coatings, since the substrate represents the part in the TWC where most of the dielectric losses occur. For this purpose, the temperature-dependent dielectric material properties of cordierite and silicon carbide (SiC) are investigated. The determined material properties are then transferred to a simulation model that calculates heat distribution and heat insertion based on the electromagnetic field distribution. The heat propagates better throughout the monolith due to the higher thermal conductivity of SiC compared to cordierite. In summary, SiC leads to a homogeneous heating of the entire catalyst material. The fact that dielectric losses of SiC decrease with temperature may help to self-limit the catalyst temperature.