Recommender Systems play a significant role in helping users identify items worthwhile for them to consume. With the increase of adopting such systems a need for systems that help a group of users identify such items for the whole group to consume together has emerged. Early research has focused on strategies that combine individual preferences to generate group preferences without much conside...

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In this paper, we present our experience in applying collaborative filtering to real-life corporate data. The quality of collaborative filtering recommendations is highly dependent on the quality of the data used to identify users’ preferences. To understand the influence that highly sparse server-side collected data has on the accuracy of collaborative filtering, we ran a series of experiments...

Collaborative filtering is one of the most successful and popular methodologies in recommendation systems. However, the traditional collaborative filtering has some limitations such as the item sparsity and cold start problem. In this paper, we propose a new methodology for solving the item sparsity problem by mapping users and items to a domain ontology. Our method uses a semantic match with t...

In recent years, the popularity of online radio has exploded. This new entertainment medium affords an opportunity not available to conventional broadcast radio: the instantaneous listening audience can be known, or what is more important, the musical tastes of the current listening audience can be known. Thus, it is possible in the new medium to tailor the playlist in real-time to the musical ...

Wikipedia is one of the largest platforms based on the concept of asynchronous, distributed, collaborative work. A systematic collaborative exploration and assessment of Wikipedia content and coverage is however still largely missing. On the one hand editors routinely perform quality and coverage control of individual articles, while on the other hand academic research on Wikipedia is mostly fo...

This paper presents a collaborative annotation framework for person identification in TV shows. The web annotation frontend will be demonstrated during the Show and Tell session. All the code for annotation is made available on github. The tool can also be used in a crowd-sourcing environment.

Tagging has gained a lot of ground as a lightweight annotation system, proven to work well in large scale deployments. This paper explores the world of collaborative tagging, and highlights some of the issues one is faced with when trying to utilize the tag meaning for knowledge extraction, with a special focus on using the tag meaning to create a user interest profile.

A number of open questions are still unanswered about whether and how dyads perform better compared to individuals on memory tasks. The literature on collaborative recall demonstrates a robust collaborative inhibition effect, where participants do worse when remembering in collaborative contexts. However, a growing body of research suggests that this inhibition can be ameliorated, or even rever...

This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan histories (pupil ID, book ID, date of loan) whereas the conventional methods require additional information such as taste information from a great number of users which is costly to obtain. To achieve this, the propose...