Navigation-based Information Seeking with User Personalization

1. Navigation-based Information Seeking with User Personalization Yuanhua Lv Nov. 28, 2007

2. Outline • Problems with V.3 • Navigation-based Information Seeking • User Personalization

3. Problems with V.3 • Inefficient and inconvenient information seeking • query -> map -> map ->map … • query1.0, query2.0, query3.0, … • save space1, save space2, then manipulate them • switching between subspaces? • complicated user interface • … • One-fit-all • Lacking user modeling, not adaptive to individual users

4. Navigation-based Information Seeking Querying Topic Map for Browsing Topic Region Navigating from a node to a child node  “Map” Navigating from a node to its neighbor  “Space Switching”

5. Navigation-based Information Seeking • Integrating Browsing and Querying in a Navigation-based information seeking framework, treating both as navigation over topic regions. • Supporting more collaboration between Browsing and Querying • Help users do space switching and space algebra easily

6. Building a multi-resolution topic map • Possible Solutions • Forming topic regions: user-defined, ontology, clustering? • Generating topic region relations • Vertical relations (children and parents): hierarchical clustering? • Horizontal relations (neighbors): frequent access pattern (People that searched behavior1 also searched behavior2), content similarity? • Labeling map node: an updated query for particular Space? Qiaozhu’s work?

7. Ranking • Possible Solutions • Ranking the search results of a topic region • Context-sensitive: utilizing rich context information, including queries, clickthroughs, browsing actions such as zoom in/out operations and neighborhood explorations • Ranking the topic regions of a map • Eager Feedback based on the user’s immediate implicit feedback information (Xuehua) More sophisticated strategy: User Personalization

8. User Personalization • Most existing information retrieval systems, including the web search engines (e.g. Google, Yahoo!), share the common problem of “one size fits all”: they do a user-generic retrieval that tries to serve everyone, and therefore never satisfies anyone. [J. Allan et. al, 2003]

9. Animal Car Apple Software Chemistry Software Jaguar Adapted from X. Shen’s SIGIR05 presentation

10. User Personalization • Recently there have been many attempts to address this issue through exploiting user’s personalized information. • K. Sugiyama et al. (2004) and J. Teevan et al. (2005) used all available user information to personalize search results. • A. Pretschner & S. Gauch (1999) and F. Liu et al. (2002) grouped user information into categories, and tried to restrict search results to these categories. • B. Tan et al. (2006) exploited relevant query records that are similar to the current search results to update query model Noisy Data

11. User Personalization • (Ongoing) We try to reduce noisy data through exploiting long-term query chains, which satisfies the following constraints: • Semantically Close: relevant to the current query • Context Sensitive: consistent with the context information • Discriminative across the current search results: high relevance to one topic and low relevance to other topics • The long-term query chains would be applied to the ranking problem of the navigation-based information seeking framework.

12. Summary • Navigation-based Information Seeking • User Personalization

13. Discussion • What exactly should the navigation interface look like? • What are some basic operations? (e.g., querying, browsing, what else?) • How do we support a user to manage a personalized information space (e.g., MyTopics, MyGenes, etc) • What user actions should we log?