Nafiseh Shabib

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In group recommendation systems, recommendations may be given to arbitrarily composed groups that may not display any particular characteristics across group members. Since individual recommendation systems can assume that the users’ previous behavior is sufficient for coming up with new recommendations, statistical analyses of user logs or user preferences(More)
Recommendation systems have received significant attention, with most of the proposed methods focusing on recommendations for single users. Recently, there are also approaches aiming at either group or context-aware recommendations. In this paper, we address the problem of contextual recommendations for groups. We exploit a hierarchical context model to(More)
We examine the problem of recommending items to ad-hoc user groups. Group recommendation in collaborative rating datasets has received increased attention recently and has raised novel challenges. Different consensus functions that aggregate the ratings of group members with varying semantics ranging from least misery to pairwise disagreement, have been(More)
Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group(More)
We propose an approach to context-aware advertising in which context is defined by the products currently used by a consumer. Unlike more traditional approaches, consumers are neither identified nor tracked; instead, products are tagged. An interesting use-case scenario for this model is a product-aware outdoor advertising system that dynamically selects a(More)
In this paper, we present a prototype of a group recommendation system for concerts. The prototype is context sensitive taking the user’s location and time into account when giving recommendations. The prototype implements three algorithms to recommend concerts by taking advantage of what users have listened to before: a collaborative filtering algorithm(More)
The 2nd International Workshop on News Recommendation and Analytics (NRA) brings together researchers on news analytics and stakeholders from the media industry. A particular focus is on news recommender systems, that tailor content from media houses and social sites to the preferences and context of individual readers. The workshop includes one invited(More)