In IT strategic outsourcing businesses, it is critical to have competent deal teams design competitive service solutions and swiftly respond to clients' requests for proposals. In this paper we present a general team recommendation framework for finding the best deal teams to pursue such engagement opportunities. Little previous work on team recommendations considers both individual and team-level features at the same time. Our proposed framework can take into account diverse individual and team features, and accommodate various cost or feature functions. We introduce a team quality metric based on a weighted linear combination of these features, the weights of which are learned using a machine learning approach by leveraging historical project outcomes. A combinatorial optimization algorithm is finally applied to search the possible solution space for the approximate best team. We report a preliminary evaluation of our framework by applying it to real-world data from strategic outsourcing businesses at a large IT service company. We also compare our approach with other existing work by using the public DBLP dataset for recommending teams in academic paper authoring.

The social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation.

Many medium and large companies maintain internal employee directories. Unfortunately, most directories only allow the lookup of individual profiles, one profile at a time. Team Analytics is a novel application that integrates information from disparate enterprise tools for groups of people. Besides accelerating the lookup process, Team Analytics also displays information that is only available in the group context, such as an organizational chart and time zone awareness. We present the Team Analytics application, its integration with our corporate email client, and results from a user survey that evaluates various aspects of the application.

Popularity based search engines have served to stagnate information retrieval from the web. Developed to deal with the very real problem of degrading quality within keyword based search they have had the unintended side effect of creating "icebergs" around topics, where only a small minority of the information is above the popularity water-line. This problem is especially pronounced with emerging information--new sites are often hidden until they become popular enough to be considered above the water-line. In domains new to a user this is often helpful--they can focus on popular sites first. Unfortunately it is not the best tool for a professional seeking to keep up-to-date with a topic as it emerges and evolves.We present a tool focused on this audience--a system that addresses the very large scale information gathering, filtering and routing, and presentation problems associated with creating a useful incremental stream of information from the web as a whole. Utilizing the WebFountain platform as the primary data engine and Really Simple Syndication (RSS) as the delivery mechanism, our "Daily Deltas" (Delta) application is able to provide an informative feed of relevant content directly to a user. Individuals receive a personalized, incremental feed of pages related to their topic allowing them to track their interests independent of the overall popularity of the topic.

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

Algorithmic tools for searching and mining the Web are becoming increasingly sophisticated and vital. In this context, algorithms that use and exploit structural information about the Web perform better than generic methods in both efficiency and reliability.We present an extensive characterization of the graph structure of the Web, with a view to enabling high-performance applications that make use of this structure. In particular, we show that the Web emerges as the outcome of a number of essentially independent stochastic processes that evolve at various scales. A striking consequence of this scale invariance is that the structure of the Web is "fractal"---cohesive subregions display the same characteristics as the Web at large. An understanding of this underlying fractal nature is therefore applicable to designing data services across multiple domains and scales.We describe potential applications of this line of research to optimized algorithm design for Web-scale data analysis.