This paper proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis---social influence and latent interests---to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative [email protected]) over state-of-the-art baselines across multiple publicly available datasets.

Entity search, a significant departure from page-based retrieval, finds data, i.e., entities, embedded in documents directly and holistically across the whole collection. This paper aims at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. We systematically evaluate our framework using a prototype over a 3TB real Web corpus with 150M pages and over 20 entity types extracted. Our experiments in two concrete application settings show our techniques of on average, 2 to 4 orders of magnitude speed-up, over the keyword-based baseline, with reasonable space overhead.

Witnessing the richness of data in document content and many ad-hoc efforts for finding such data, we propose a Data-oriented Content Query System(DoCQS), which is oriented towards fine granularity data of all types by searching directly into document content. DoCQS uses the relational model as the underlying data model, and offers a powerful and flexible Content Query Language(CQL) to adapt to diverse query demands. In this demonstration, we show how to model various search tasks by CQL statements, and how the system architecture efficiently supports the CQL execution. Our online demo of the system is available at http://wisdm.cs.uiuc.edu/demos/docqs/.

As the Web provides rich data embedded in the immense contents inside pages, we witness many ad-hoc efforts for exploiting fine granularity information across Web text, such as Web information extraction, typed-entity search, and question answering. To unify and generalize these efforts, this paper proposes a general search system--Data-oriented Content Query System(DoCQS)--to search directly into document contents for finding relevant values of desired data types. Motivated by the current limitations, we start by distilling the essential capabilities needed by such content querying. The capabilities call for a conceptually relational model, upon which we design a powerful Content Query Language (CQL). For efficient processing, we design novel index structures and query processing algorithms. We evaluate our proposal over two concrete domains of realistic Web corpora, demonstrating that our query language is rather flexible and expressive, and our query processing is efficient with reasonable index overhead.

As the Web has evolved into a data-rich repository, with the standard page view, current search engines are increasingly inadequate. While we often search for various data entities (e.g. phone number, paper PDF, date), today's engines only take us indirectly to pages. Therefore, we propose the concept of entity search, a significant departure from traditional document retrieval. Towards our goal of supporting entity search, in the WISDM project at UIUC we build and evaluate our prototype search engine over a 2TB Web corpus. Our demonstration shows the feasibility and promise of a large-scale system architecture to support entity search.