Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval

In this paper we present a number of methods for re-ranking video search results in order to introduce diversity into the set of search results. The usefulness of these approaches is evaluated in comparison with similarity based measures, for the TRECVID 2007 collection and tasks [11]. For the MAP of the search results we find that some of our approaches perform as well as similarity based methods. We also find that some of these results can improve the P@N values for some of the lower N values. The most successful of these approaches was then implemented in an interactive search system for the TRECVID 2008 interactive search tasks. The responses from the users indicate that they find the more diverse search results extremely useful

Diverse Weighted Bipartite b-Matching

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research. In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a supermodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice

User centred evaluation of a recommendation based image browsing system

In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful

Design a Product Aspect Ranking Framework and Its Applications

Today lots of consumer reviews about products are present on the Internet. Consumer reviews reflect important knowledge about product that will be helpful for firms as well as users. The reviews are most of times not organized properly that going to difficulties in information and knowledge gaining. We proposes a product aspect ranking framework, that automatically determines the important aspects of products by using online consumer reviews, improving the usability of the frequent given reviews. The important aspects about product are determined depends on two observations: 1) the important aspects are often comment by numerous consumers 2) consumer opinions on the important aspects largely affect their overall opinions on the product. With the help of given consumer reviews of a product, we firstly identify aspects of product by shallow dependency parser and identify consumer opinions on these aspects by a sentiment classifier. After that developing a probabilistic aspect ranking to grab the importance of aspects by concurrently considering aspect frequency and the impact of consumer opinions given to every aspect over their allover opinions. We apply this ranking framework to two real-world applications, i.e., document-level sentiment classification and extractive review collection, that show significant performance improvements, that leads in giving the strength of product aspect ranking in promoting real-world applications

Nouvelle approche classificatoire appliquée au web, Une validation expérimentale : représentation des sciences de l'information et de la communication sur le web

Le Web est un " é-cosystème " [5] documentaire composé de pages web en interaction. Les moteurs de recherche majeurs restituent, suite à la requête d'un internaute, une information organisée sous forme d'une liste de pages disjointes de laquelle il est difficile de dégager une vision d'ensemble. Différentes familles d'outils ont été créés pour répondre à ce problème : les outils de classification en font partie. Après un état de l'art des outils de classification automatique de corpus web, cette communication présente une méthode de classification originale qui fait ensuite l'objet d'une validation expérimentale

Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning

PACLIC 23 / City University of Hong Kong / 3-5 December 200