A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user his...

In this paper, natural language (nl) dialogue is suggested as interaction technique for personalized EPGs to handle a variety of well-known problems. nl interaction addresses the new-user cold-start problem since the necessary user model can be gathered gracefully, without the high initial user effort prominent in traditional recommendation systems. It is argued that nl interaction enhances the...

When building a recommender system, how can we ensure that all items are modeled well? Classically, recommender systems are built, optimized, and tuned to improve a global prediction objective, such as root mean squared error. However, as we demonstrate, these recommender systems often leave many items badly-modeled and thus under-served. Further, we give both empirical and theoretical evidence...

Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multidomain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold...

We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm,which can capture collaborative effects. CAB can be easily deployed in a real-world recommendation system, where multi-armed bandits have been shown toperform well in particular with respect to the cold-start problem. CAB utilizes acontext-aware clustering augmented by exploration-exploitation strategies...

This article describes a data driven method for deriving the relationship between personality and media preferences. A qunatifiable representation of such a relationship can be leveraged for use in recommendation systems and ameliorate the “cold start” problem. Here, the data is comprised of an original collection of 1,316 Okcupid dating profiles. Of these profiles, 800 are labeled with one of ...

In this paper, we present TV Scout, a recommendation system providing users with personalized TV schedules. The TV Scout architecture addresses the “cold-start” problem of information filtering systems, i.e. that filtering systems have to gather information about the user’s interests before they can compute personalized recommendations. Traditionally, gathering this information involves upfront...

Item recommendation task predicts a personalized ranking over a set of items for individual user. One paradigm is the rating-based methods that concentrate on explicit feedbacks and hence face the difficulties in collecting them. Meanwhile, the ranking-based methods are presented with rated items and then rank the rated above the unrated. This paradigm uses widely available implicit feedback bu...

Review history is widely used by recommender systems to infer users’ preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users’ preferences. Meanwhile, with the fast dev...

On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient real users for policy training. We propose a novel framework, companion teaching, to include a human teacher in the on-line dialogue policy training loop to address the cold start p...