The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8.

The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.

Online social networks and mash-up services create opportunities to connect different web services otherwise isolated. Specifically in the case of news, users are very much exposed to news articles while performing other activities, such as social networking or web searching. Browsing behavior aimed at the consumption of news, especially in relation to the visits coming from other domains, has been mainly overlooked in previous work. To address that, we build a BrowseGraph out of the collective browsing traces extracted from a large viewlog of Yahoo News (0.5B entries), and we define the ReferrerGraph as its subgraph induced by the sessions with the same referrer domain. The structural and temporal properties of the graph show that browsing behavior in news is highly dependent on the referrer URL of the session, in terms of type of content consumed and time of consumption. We build on this observation and propose a news recommender that addresses the cold-start problem: given a user landing on a page of the site for the first time, we aim to predict the page she will visit next. We compare 24 flavors of recommenders belonging to the families of content-based, popularity-based, and browsing-based models. We show that the browsing-based recommender that takes into account the referrer URL is the best performing, achieving a prediction accuracy of 48% in conditions of heavy data sparsity.

This paper presents a strategy to identify the geographic location of videos. First, it relies on a multi-modal cascade pipeline that exploits the available sources of information, namely the user's upload history, his social network and a visual-based matching technique. Second, we present a novel divide & conquer strategy to better exploit the tags associated with the input video. It pre-selects one or several geographic area of interest of higher expected relevance and performs a deeper analysis inside the selected area(s) to return the coordinates most likely to be related to the input tags. The experiments were conducted as part of the MediaEval 2012 Placing Task. Our approach, which differs significantly from the other submitted techniques, achieves the best results on this benchmark when considering the same amount of external information, i.e. when not using any gazetteers nor any other kind of external information.

In general, user browsing behavior has been examined within specific tasks (e.g., search), or in the context of particular web sites or services ( e.g., in shopping sites). However, with the growth of social networks and the proliferation of many different types of web services ( e.g., news aggregators, blogs, forums, etc.), the web can be viewed as an ecosystem in which a user's actions in a particular web service may be influenced by the service she arrived from ( e.g., are users browsing patterns similar if they arrive at a website via search or via links in aggregators?). In particular, since photos in services like Flickr are used extensively throughout the web, it is common for visitors to the site to arrive via links in many different types of web sites. In this paper, we depart from the hypothesis that visitors to social sites such as Flickr behave differently depending on where they come from. For this purpose, we analyze a large sample of Flickr user logs to discover social photo navigation patterns. More specifically, we classify pages within Flickr into different categories ( e.g., "add a friend page", "single photo page," etc.), and by clustering sessions discover important differences in social photo navigation that manifest themselves depending on the type of site users visit before visiting Flickr. Our work examines photo navigation patterns in Flickr for the first time taking into account the referrer domain. Our analysis is useful in that it can contribute to a better understanding of how people use photo services like Flickr, and it can be used to inform the design of user modeling and recommendation algorithms, among others.

Ranking of images is difficult because many factors determine their importance (e.g., popularity, quality, entertainment value, context, etc.). In social media platforms, ranking also depends on social interactions and on the visibility of the images both inside and outside those platforms. In this context, the application of standard ranking methods is not clearly understood, and neither are the subtleties associated with taking into account social interaction, internal, and external factors. In this paper, we use a large Flickr dataset and investigate these factors by performing an in-depth analysis of several ranking algorithms using both internal (i.e., within Flickr) and external (i.e., links from outside of Flickr) factors. We analyze rankings given by common metrics used in image retrieval (e.g., number of favorites), and compare them with metrics based on page views (e.g., time spent, number of views). In addition, we represent users' navigation by a graph and combine session models with some of these metrics, comparing with PageRank and BrowseRank. Our experiments show significant differences between the rankings, providing insights on the impact of social interactions, internal, and external factors in image ranking.

We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. Given a query image and a large logo database, the goal is to recognize the logo contained in the query, if any. We locally group features in triples using multi-scale Delaunay triangulation and represent triangles by signatures capturing both visual appearance and local geometry. Each class is represented by the union of such signatures over all instances in the class. We see large scale recognition as a sub-linear search problem where signatures of the query image are looked up in an inverted index structure of the class models. We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes.