Hierarchical Interest Graph
Contents
Abstact
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming.
In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as Hierarchical Interest Graph. To create such graphs, we utilize user's Twitter data to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then use an adaptation of spreading activation theory to assign user interest score (or weights) to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of user's explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted categories in the graph being relevant to a given user's interests.
Approach
The goal of our approach is to construct a Hierarchical Interest Graph (HIG) for a Twitter user. To accomplish this our system performs the following steps:
- Hierarchy Pre-processor<\b> converts the Wikipedia Category Graph (WCG) into a hierarchy
- <b>User Interests Generator<\b> spots and scores the Primitive Interests from tweets of a user.
- <b>Interest Hierarchy Generator<\b> maps the scored Primitive Interests to Wikipedia Hierarchy to infer the HIG of the user.
