Recommender systems: Trends and frontiers

Trends in Content-Based Recommender Systems

This article reports on the CBRecSys 2015 workshop, the second edition of the workshop on new trends in content-based recommender systems, co-located with RecSys 2015 in Vienna, Austria. Content-based recommendation has been applied successfully in many different domains, but it has not seen the same level of attention as collaborative filtering techniques have. Nevertheless, there are many rec...

Personalised Information Gathering and Recommender Systems: Techniques and Trends

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-...

Recommender Systems Using Social Network Analysis: Challenges and Future Trends

Content-based Recommender Systems: State of the Art and Trends

Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and...

Trust-aware Recommender Systems Chapter 1 Trust-aware Recommender Systems Trust-aware Recommender Systems 1.1 Recommender Systems Trust-aware Recommender Systems

Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...