With the popularity of microblogging services such as Twitter in recent years, an increasing number of users use these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and third-party ap...

We might identify three canonical approaches to leveraging some degree of semantic analysis to support information access, one that starts with queries as they exist today, one that starts with existing knowledge representations, and one that starts with semantic analysis of documents. These correspond to what have been called semantic matching, semantic search, and machine reading. In this tal...

Today trust is a key factor in distributed and collaborative environments aimed to model participating entities’ behavior, and to foresee and predict their expected actions in the future. Yet, prior to the first interaction of a new party in the system, trust and reputation models face a great challenge: how to assign an accurate initial reputation score to a newcomer? The answer needs to tackl...

We consider information filtering, in which we face a stream of items too voluminous to process by hand (e.g., scientific articles, blog posts, emails), and must rely on a computer system to automatically filter out irrelevant items. Such systems face the exploration vs. exploitation tradeoff, in which it may be beneficial to present an item despite a low probability of relevance, just to learn...

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an ...

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose kernel based recommender (KBR) algorithms that solve the recommender system problem based on a novel structure learning technique. This paper makes contribution on the followings: we show how (1) user-based and item-based versio...

Effective acquisition of descriptive semantics for images is still an open issue today. Crowd-based human computation represents a family of approaches able to provide large scale metadata with decent quality. Within this field, games with a purpose (GWAP) have become increasingly important, as they have the potential to motivate contributors to the process through entertainment. However, the e...

We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based pa...

The vast majority of current recommender systems focus on a single domain. Netflix makes personalized recommendations of movies and TV series, and Last.fm suggests music compositions and artists. E-commerce sites like Amazon, however, may take benefit from exploiting the user’s preferences on diverse types of items to provide recommendations in different but somehow related domains. Recommendat...

We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the cold-start issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results.