The cold-start problem is a major factor that limits the effectiveness of recommendation systems. Having too few available interaction records brings series challenges when predicting user preferences. At present, there are two main kinds strategies for solving this from different perspectives. One cross-domain (CDR), which introduces additional information by domain knowledge propagation with ...

Abstract Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue customer satisfaction is dependent on user’s ability to discover desirable items in large catalogues. As number users a platform grows, computational complexity sparsity problem constitute important challenges for any recommendation algorithm. In addition, most widel...

Recommender systems are popular information filtering systems used in various domains. Cold-start problem is a key challenge in a recommender system. In newitem/existing-user case of the cold-start problem, which is recommendation of a recentlyarrived item to a user with historical data, finding links between existing items with recently-arrived items is critical. Using VideoLectures.net Cold-S...

Recently, software crowdsourcing platforms, which provide paid tasks for developers, become attractive to both employers and developers. Developers expect to find tasks that match their interests and capabilities via crowdsourcing platforms, and thus recommender systems play important roles in these platforms. However, we still face several challenges when building a recommender system for a cr...

The development of gasoline direct injection (GDI) engines has provided a strong alternative to port fuel injection engines as they offer increased power output and better fuel economy and carbon dioxide emissions. However, particulate matter (PM) emission reduction from GDI still remains a challenge that needs to be addressed in order to fulfil the increasingly stricter environmental regulatio...

In this study, experimental constant-current cold starts were performed on a polymer electrolyte fuel cell from −10°C to characterize high-frequency resistance behavior, water motion, and ice accumulation before, during, and after cold start. A diagnostic method for rapid and repeatable cold starts was developed and verified. Cold-start performance is found to be optimized when cell resistance ...

Collaborative Filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting a user’s preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all...

Traditionally, latent factor models have been the most successful techniques to build recommendation systems. While the key is to capture the user interests effectively, most research is focused on learning latent factors under cold-start and data sparsity situations. Our work brings a complementary approach to the previous studies showing that understanding the semantic aspects of latent facto...

EEcient instruction and data caches are extremely important for achieving good performance from modern high performance processors. Conventional cache architectures exploit locality, but do so rather blindly. By forcing all references through a single structure, the cache's eeectiveness on many references is reduced. This paper presents a selective caching scheme for improving cache performance...

This paper introduces minimal subset evaluation (MSE) as a way IO reduce time spent on large-structure warm-up during the fastforwarding portion of processor simulations. Warm up is common1.y used prior to full-detail simulation to avoid cold-start bias in large structures like caches and branch predictors. Unfortunately, warm up can be very time consuming, o fen representing 50% or more of tot...