1. INTRODUCTION
If we imagine seeing the world from the perspective of a search
engine, our only view of user behavior would be the stream of
queries users produce. Search engine designers often adopt this
perspective, studying these query streams and trying to optimize
the engines based on such factors as the length of a typical query.
Yet this same perspective has prevented us from looking beyond
the query, at why the users are performing their searches in the
first place.
The “why” of user search behavior is actually essential to
satisfying the user’s information need. After all, users don’t sit
down at their computer and say to themselves, “I think I’ll do
some searches.” Searching is merely a means to an end – a way to
satisfy an underlying goal that the user is trying to achieve. (By
“underlying goal,” we mean how the user might answer the
question “why are you performing that search?”) That goal may 数据挖掘研究院
be choosing a suitable wedding present for a friend, learning
which local colleges offer adult education courses in pottery,
seeing if a favorite author’s new book has been released, or any number of other possibilities. In fact, in some cases the same
query might be used to convey different goals – for example, the
query “ceramics” might have been used in any of the three
situations above (assuming it is also the title of the book in
question).
What difference would it make if the search engine knew the
user’s goal? At the very least, the engine might provide a user
experience tailored toward that goal. For example, the display of
relevant advertising might be welcome in a shopping context, but
unwelcome in a research context. In fact, we have argued
elsewhere [10] that goal-sensitivity will be one of the crucial
factors in future search user interfaces. But the potential to
capitalize on this goal sensitivity goes beyond the user interface.
The underlying relevance-ranking algorithms that determine
which results are presented to users might differ depending on the
search goal. For example, queries that express a need for advice
might rely more on usage- or connectivity-based relevance
factors, while those involving open-ended research might weight
traditional information retrieval measures (such as term
frequency) more highly.
Our premise is that web searches reflect a diverse set of
underlying user goals, and that knowledge of those goals offers
the prospect of future improvements to web search engines.
Achieving these improvements is an ambitious project involving
three primary tasks. First, we need to create a conceptual
framework for user goals. Second, we need a way for search
engines to associate user goals with queries. Third, we need to
modify the engines in order to exploit the goal information.
In this paper we focus on the first task, and the initial parts of the 数据挖掘研究院
second: characterizing user search goals and examining the
problem of inferring goals from query behavior. We begin in
section 2 by looking at previous work on understanding
information-seeking behavior. Next, in section 3, we describe our
model of search goals. In section 4, we review the methodology
used to classify queries using our model, and we provide some
results from this analysis. We conclude with some final thoughts
about the applicability of this work.
