Search

Social annotation is an intuitive, on-line, collaborative process through which each element of a collection of resources (e.g., URLs, pictures, videos, etc.) is associated with a group of descriptive keywords, widely known as tags. Each such group is a concise and accurate summary of the relevant resource's content and is obtained via aggregating the opinion of individual users, as expressed in the form of short tag sequences. The availability of this information gives rise to a new searching paradigm where resources are retrieved and ranked based on the similarity of a keyword query to their accompanying tags.In this paper, we present a principled and efficient search and resource ranking methodology that utilizes exclusively the user-assigned tag sequences. Ranking is based on solid probabilistic foundations and our growing understanding of the dynamics and structure of the social annotation process, which we capture by employing powerful interpolated n-gram models on the tag sequences. The efficiency and applicability of the proposed solution to large data sets is guaranteed through the introduction of a novel and highly scalable constrained optimization framework, employed both for training and incrementally maintaining the n-gram models.We experimentally validate the efficiency and effectiveness of our solutions compared to other applicable approaches. Our evaluation is based on a large crawl of del.icio.us, numbering hundreds of thousands of users and millions of resources, thus demonstrating the applicability of our solutions to real-life, large scale systems. In particular, we demonstrate that the use of interpolated n-grams for modeling tag sequences results in superior ranking effectiveness, while the proposed optimization framework is superior in terms of performance both for obtaining ranking parameters and incrementally maintaining them.

User generated content has been fueling an explosion in the amount of available textual data. In this context, it is also common for users to express, either explicitly (through numerical ratings) or implicitly, their views and opinions on products, events, etc. This wealth of textual information necessitates the development of novel searching and data exploration paradigms.In this paper we propose a new searching model, similar in spirit to faceted search, that enables the progressive refinement of a keyword-query result. However, in contrast to faceted search which utilizes domain-specific and hard-to-extract document attributes, the refinement process is driven by suggesting interesting expansionsof the original query with additional search terms. Our query-driven and domain-neutralapproach employs surprising word co-occurrence patterns and (optionally) numerical user ratings in order to identify meaningful top-kquery expansions and allow one to focus on a particularly interesting subset of the original result set.The proposed functionality is supported by a framework that is computationally efficient and nimble in terms of storage requirements. Our solution is grounded on Convex Optimization principles that allow us to exploit the pruning opportunities offered by the natural top-kformulation of our problem. The performance benefits offered by our solution are verified using both synthetic data and large real data sets comprised of blog posts.