Understanding User Goals in Web Search University of Seoul Computer Science Database Lab. Min Mi-young

Database Lab.2 Contents Introduction Related Work A Framework For Search Goals Associating Goals With Queries Manual query classification Results Future work Conclusions

Introduction Focus on in previous works How people search What they are searching for Not why they are searching Suggests “navigational” searches User behavior would be the stream of queries users produce Studying these queries and Optimizing the engines based on such as the length of a typical query Prevent from looking beyond query “Why” is essential to satisfy user’s information need Database Lab.3

Introduction Goal : “Why are you performing that search?” (cf) “ceramics” For choosing a suitable present for a friend Learning which colleges offer adult education courses in pottery Seeing if a favorite author’s new book has been released, etc If we knew user’s goal…? At the very least, engine provide a user experience tailored toward that goal Database Lab.4

Introduction Premise Web searches reflect a diverse set of underlying user goals Knowledge of goals offers the prospect of future improvements to web search engines Involve three tasks 1. Need to create a conceptual framework for user goals 2. Need a way for search engines to associate user goals with queries 3. Need to modify the engines in order to exploit the goal information Focus on first task, & initial parts of the second Characterizing user search goals & examining the problem of inferring goals from query behavior Database Lab.5

Related Work Bates [4] look at the different ways in which people performed searches Later proposed ways to characterize the overall search process [5] Belkin’s Anomalous States of Knowledge (ASK) framework Attempt to model the cognitive state of the user And then translate this understanding into a practical design for an information retrieval system [6] An analysis of some of the different types of information needs of different users Database Lab.6

Related Work Silverstein et al Conducted an analysis of query logs from the AltaVista search engine Confirming some of the original findings of web search use, such as the predominance of very short queries [11] Ongoing research of Spink and her colleagues Analyzed query logs of the Excite search engine from 1997, 1999, and 2991 [13] Database Lab.7

Related Work Prior to the advent of the worldwide web, search engine designers Assume that users had an “informational” goal in mind Goals underlying web searches are many and varied Above these helped us to understand ‘what’ users are searching for and ‘how ‘their information- seeking process works Database Lab.8

Related Work “Broder’s taxomony of web search” [7] Idea Traditional notion of an “information need” might not adequately describe web searching Came up with a trichotomy of web search “types” : navigational, informational, transactional Navigational : are intended to find a specific web site that the user has in mind Informational : are intended to find information about a topic Transactional : are intended to “perform some web-mediated activity” Database Lab.9

A Framework For Search Goals First task To understand the space of use goals Come up with a framework that could identify and organize a manageable set of canonical goal categories Goal categories must encompass the majority of actual goals users have in mind when searching Develop the goal framework 1. Looked at a sample of queries from the AltaVista search engine 2. Brainstormed a variety of goal possibilities, based on our own experiences 3. Resulted in a flat list of goals Served as a basis for an initial goal classification framework Database Lab.10

A Framework For Search Goals 4. which used to categorize a sample of queries 5. Revised the framework to accommodate the results of the classification test Categories were modified, (or new add, drop, merge, split) 6. Classify-refine process was repeated three times with a new set of queries Database Lab.11

A Framework For Search Goals Resulting goal framework Goals of Hierarchical structure Resemble Broder’s trichotomy, more general “resource” category Database Lab.12

A Framework For Search Goals Database Lab.13

Associating Goals With Queries Two ways User identify the goal explicitly through the use interface Google’s “I’m feeling lucky”, in which users implicitly identify their goal as “navigate to a specific web site” System can attempt to infer the goal automatically Involve automatic classification using statistical or machine learning methods Database Lab.14

Manual Query Classification User goals can be deducted from looking at user behavior available to the search engine Included in behavior The query itself The results returned by the search engine Results clicked on by the user Further searches or other actions by the user To facilitate task of manual classification Created a research tool that provided these four types of information for sets of queries taken from the AltaVista query logs Database Lab.15

Database Lab.16 A screen shot of the classification tool interface Query : “kelly blue book” Links which lead to the search results that appear when the query is executed Six seconds later, user entered a new, more specific query on the same topic Eight seconds later, clicked which is the home page of the Kelly Blue Book

Manual Query Classification Query : final fantasy (name of popular computer games) User examined the result list for 36 seconds, then visited web site “an unofficial guide to final fantasy” About a minute later, user returned to original query, then chose a diff site, “Eyes on Final Fantasy” Indicates that user was not interested in buying the game, but simply information about it Database Lab.17

Results Database Lab.18

User goals can be deducted from looking at user behavior available to the search engine Included in behavior The query itself The results returned by the search engine Results clicked on by the user Further searches or other actions by the user To facilitate task of manual classification Created a research tool that provided these four types of information for sets of queries taken from the AltaVista query logs Database Lab.19

Future Work Certain limitations Have no way of knowing conclusively whether the goal we inferred for a query is in fact the user’s actual goal Combine with user studies, including qualitative data AltaVista user population may not be representative of search engine users in general This may account for some of the user behavior we saw Extend research to Yahoo! Database Lab.20

Conclusion Need to take into account more knowledge of user behavior – not how, but why! Achievement Created a framework for understanding the underlying goals of search Demonstrated the framework can be used to associate goals with queries given limited information Conclusion Navigational queries appear to much less prevalent than generally believed Many queries appear to be motivated by a unexplored goal involving the need to obtain online and offline resources Database Lab.21