Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process

Visual Chunking: A List Prediction Framework for Region-Based Object Detection

We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

Raising argument strength using negative evidence: A constraint on models of induction

Both intuitively, and according to similarity-based theories of induction, relevant evidence raises argument strength when it is positive and lowers it when it is negative. In three experiments, we tested the hypothesis that argument strength can actually increase when negative evidence is introduced. Two kinds of argument were compared through forced choice or sequential evaluation: single positive arguments (e.g., “Shostakovich’s music causes alpha waves in the brain; therefore, Bach’s music causes alpha waves in the brain”) and double mixed arguments (e.g., “Shostakovich’s music causes alpha waves in the brain, X’s music DOES NOT; therefore, Bach’s music causes alpha waves in the brain”). Negative evidence in the second premise lowered credence when it applied to an item X from the same subcategory (e.g., Haydn) and raised it when it applied to a different subcategory (e.g., AC/DC). The results constitute a new constraint on models of induction

Groups without cultured representatives dominate eukaryotic picophytoplankton in the oligotrophic South East Pacific Ocean

Background: Photosynthetic picoeukaryotes (PPE) with a cell size less than 3 µm play a critical role in oceanic primary production. In recent years, the composition of marine picoeukaryote communities has been intensively investigated by molecular approaches, but their photosynthetic fraction remains poorly characterized. This is largely because the classical approach that relies on constructing 18S rRNA gene clone libraries from filtered seawater samples using universal eukaryotic primers is heavily biased toward heterotrophs, especially alveolates and stramenopiles, despite the fact that autotrophic cells in general outnumber heterotrophic ones in the euphotic zone. Methodology/Principal Findings: In order to better assess the composition of the eukaryotic picophytoplankton in the South East Pacific Ocean, encompassing the most oligotrophic oceanic regions on earth, we used a novel approach based on flow cytometry sorting followed by construction of 18S rRNA gene clone libraries. This strategy dramatically increased the recovery of sequences from putative autotrophic groups. The composition of the PPE community appeared highly variable both vertically down the water column and horizontally across the South East Pacific Ocean. In the central gyre, uncultivated lineages dominated: a recently discovered clade of Prasinophyceae (IX), clades of marine Chrysophyceae and Haptophyta, the latter division containing a potentially new class besides Prymnesiophyceae and Pavlophyceae. In contrast, on the edge of the gyre and in the coastal Chilean upwelling, groups with cultivated representatives (Prasinophyceae clade VII and Mamiellales) dominated. Conclusions/Significance: Our data demonstrate that a very large fraction of the eukaryotic picophytoplankton still escapes cultivation. The use of flow cytometry sorting should prove very useful to better characterize specific plankton populations by molecular approaches such as gene cloning or metagenomics, and also to obtain into culture strains representative of these novel groups