Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Search queries are often short, and the underlying user intent may be ambiguous. This
makes it challenging for search engines to predict possible intents, only one of which may …

Automatic spelling correction has been receiving sustained research attention. Although
each article contains a brief introduction to the topic, there is a lack of work that would …

With the rapid adoption of online shopping, academic research in the eCommerce domain
has gained traction. However, significant research challenges remain, spanning from classic …

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender
systems. The J-NCF model applies a joint neural network that couples deep feature learning …

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …

With the increase in Technology Enhanced Learning (TEL), the effective retrieval and
availability of Learning Objects (LOs) for course designers is a significant concern. Text …

Asking clarifying questions in response to search queries has been recognized as a useful
technique for revealing the underlying intent of the query. Clarification has applications in …

Optimizing ranking systems based on user interactions is a well-studied problem. State-of-
the-art methods for optimizing ranking systems based on user interactions are divided into …