A recommender system suggests items to a user for a given query by personalizing the recommendations based on the user interests. User personalization is usually done by asking users either to rate items or specify their interests. Generally users do not like to rate items; an alternative approach would be to implicitly track user’s behaviour by observing their actions. In this paper, we build ...

Examining and evaluating complex ecological communites is a popular study area of significant ecological value. Mutualistic plant-pollinator networks are an especially important community because of the crucial role of pollinator in food production, yet much is still unkown about the underlying community structures of these networks, as well as their stability under enviornmental pertubation. W...

Research into recommender systems has focused on the importance of considering a variety of users’ inputs for an efficient capture of their main interests. However, most collaborative filtering efforts are related to latent factors and implicit feedback, which do not consider the metadata associated with both items and users. This article proposes a hybrid recommender model which exploits impli...

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are des...

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the user did not consume an item because she was unaware of it. In this paper we ...

We explore the use of eye movements as a source of implicit relevance feedback information. We construct a controlled information retrieval experiment where the relevance of each text is known, and test usefulness of implicit relevance feedback with it. If perceived relevance of a text can be predicted from eye movements, eye movement signal must contain information on the relevance. The result...

In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback from negated information in noisy natural language search queries. We begin by studying the extent to which negations occur in clinical texts and quantify their detrimental effect on retrieval performance. Subsequently, we present a number of query reformulation an...