Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains where personalization is highly valued. Such recommender systems typically base their suggestions on (1) collaborative data encoding which users like which items, and/or (2) content data describing ite...

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clea...

This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential aims capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online scenarios. However, previous methods have trouble recommending items, which are prevalent those As there is generally no side information task, could not be ap...

Sequential prediction is a fundamental task for Web applications. Due to the insufficiency of user feedbacks, sequential prediction usually suffers from the cold start problem. There are two kinds of popular approaches based on matrix factorization (MF) and Markov chains (MC) for item prediction. MF methods factorize the user-item matrix to learn general tastes of users. MC methods predict the ...

In low ambient temperatures the engine will have poor output torque and power generation at the first cycles of its performance, long start time and huge noxious emissions because of insufficient temperature and quality of air-fuel mixture and failure of flame to burn the charge. It is noted that any attempt to increase the charge temperature leads to better combustion conditions. By applying t...

Cold start recommendations are important because they help build user loyalty, which is the key to the success of e-services and e-commerce systems. Recommending useful information for new users generally creates a sense of belonging and loyalty, and encourages them to visit e-commerce systems frequently. However, as new users take time to become familiar with recommendation systems, the system...

Personalized language models are useful in many applications, such as personalized search and personalized recommendation. Nevertheless, it is challenging to build a personalized language model for cold start users, in which the size of the training corpus of those users is too small to create a reasonably accurate and representative model. We introduce a generalized framework to enrich the per...

The mechanisms by which sensible heat fluxes (SHFs) alter cold pool characteristics and dissipation rates are investigated in this study using idealized two-dimensional numerical simulations and an environment representative of daytime, dry, continental conditions. Simulations are performed with no SHFs, SHFs calculated using a bulk formula, and constant SHFs for model resolutions with horizont...