Analyzing the traces left by a meme of information propagating through a social network or by a user browsing a website can help to unveil the structure and dynamics of such complex networks. This may in turn open the door to concrete applications, such as finding influential users for a topic in a social network, or detecting the typical structure of a web browsing session that leads to a product purchase. In this paper we define the problem of mining summaries of propagations as a constrained pattern-mining problem. A propagation is a DAG where an entity (e.g., information exchanged in a social network, or a user browsing a website) flows following the underlying hierarchical structure of the nodes. A summary is a set of propagations that (i) involve a similar population of nodes, and (ii) exhibit a coherent hierarchical structure when merged altogether to form a single graph. The first constraint is defined based on the Jaccard coefficient, while the definition of the second one relies on the graph-theoretic concept of "agony" of a graph. It turns out that both constraints satisfy the downward closure property, thus enabling Apriori-like algorithms. However, motivated by the fact that computing agony is much more expensive than computing Jaccard, we devise two algorithms that explore the search space differently. The first algorithm is an Apriori-like, bottom-up method that checks both the constraints level-by-level. The second algorithm consists of a first phase where the search space is pruned as much as possible by exploiting the Jaccard constraint only, while involving the second constraint only afterwards, in a subsequent phase. We test our algorithms on four real-world datasets. Quantitative results reveal that the choice of the most efficient algorithm depends on the selectivity of the two constraints. Qualitative results show the relevance of the extracted summaries in a number of real-world scenarios.

The behaviour, goals, and intentions of users while searching for images in large scale online collections are not well understood, with image search log analysis providing limited insights, in part because they tend only to have access to user search and result click information. In this paper we study user search behaviour in a large photo-sharing platform, analyzing all user actions during search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image search. We show that search behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image search.

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.

We study discriminative clustering for market segmentation tasks. The underlying problem setting resembles discriminative clustering, however, existing approaches focus on the prediction of univariate cluster labels. By contrast, market segments encode complex (future) behavior of the individuals which cannot be represented by a single variable. In this paper, we generalize discriminative clustering to structured and complex output variables that can be represented as graphical models. We devise two novel methods to jointly learn the classifier and the clustering using alternating optimization and collapsed inference, respectively. The two approaches jointly learn a discriminative segmentation of the input space and a generative output prediction model for each segment. We evaluate our methods on segmenting user navigation sequences from Yahoo! News. The proposed collapsed algorithm is observed to outperform baseline approaches such as mixture of experts. We showcase exemplary projections of the resulting segments to display the interpretability of the solutions.

PRiSMA is an image search application for tablet and desktop devices intended to facilitate and promote the searching of images in parallel. With an intuitive user interface, users can branch their queries into multiple horizontal sliding strips to simultaneously explore different perspectives of large image collections (e.g., colors, geographical location or topic). Strips can be easily created, tailored, merged, and removed, allowing users to effectively perform multiple queries and manage the results in a dynamic and orderly fashion. With PRiSMA we aim to explore the potential and limitations of parallel image search from a user perspective.

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.

The accumulation of large collections of social media data poses new challenges for the design of exploratory experiences, such as when a user browses through a collection to discover content (e.g. exploring photo collections, network of friends, etc). Cardinality and characteristics of the set, together with volatility of the information, resulting from fast and continuous creation, deletion and updating of entries, trigger novel research questions. In this context, we plan to investigate and contribute to the data analysis, and user interface design of exploratory experiences. The proposed approach is an iterative process where analysis and design phases are performed in cycles. The long-term vision is to understand the underlying reasoning in order to be able to automatically replicate it.

This work describes a general approach to multimedia playlist generation and description and an application of the approach to music information retrieval. The example of system that we implemented updates a musical playlist on the fly based on prior information (musical preferences); current descriptors of the song that is being played; and fine-grained and semantically rich descriptors (descriptors of user's gestures, of environment conditions, etc.). The system incorporates a learning system that infers the user's preferences. Subjective tests have been conducted on usability and quality of the recommendation system.