Discovering Context-aware Influential Objects
Yangpai Liu, Huiping Cao, Yifan Hao, Peng Han, Xinda Zeng · 2012
It is very helpful for a user to get a moderate amount of information highly related to his/her immediate context (e.g., location, time, discussion topics) during the exploration of digital object collections (e.g., articles, web pages, blogs). For instance, in investigating a research topic, a researcher may be very interested in finding articles that are most related to the articles he/she already read on this topic, which we consider as “context” in this paper. To facilitate users' exploration, we introduce the problem of discovering Context-aware Influential Objects (CIO) from a collection of digital objects with influence relationships. Although there is a large amount of work in detecting direct influence degree between objects to denote how strong an object influences others, very few works utilize such direct influence to find influential objects for a context. To discover CIOs for a context consisting of several objects of a user's interest, the first challenge is to meaningfully measure the collective influence of an object over a context considering both the direct influence and the indirectly derived influence, which is not taken into consideration by most “query by example” approaches. We propose an aggregation framework to formulate the collective influence among objects by leveraging both direct and indirect influence. The second challenge is to discover CIOs efficiently. We present three approaches to calculate collective influence of an object over a context from an influence graph. In particular, the first approach utilizes the breadth-first-search paradigm; the other approaches make use of the topological sorting of graph nodes and perform context-aware search using push and pull mechanisms. We show experimental results on real datasets to demonstrate the effectiveness and efficiency of the proposed methodologies.