Rockstar Consortium US LP et al v. Google Inc

Filing 92

MOTION for Leave to File A Supplemental Brief In Response To Googles Motion To Transfer In Light Of Newly-Acquired Evidence by NetStar Technologies LLC, Rockstar Consortium US LP. (Attachments: # 1 Text of Proposed Order Granting Motion for Leave to File a Supplemental Brief In Response to Google's Motion to Transfer in Light of Newly-Acquired Evidence, # 2 Supplement Plaintiffs' Supplemental Brief In Response to Google's Motion to Transfer, # 3 Affidavit of Amanda Bonn In Support of Plaintiffs' Supplemental Brief In Response to Google's Motion to Transfer, # 4 Exhibit 1, # 5 Exhibit 2, # 6 Exhibit 3, # 7 Exhibit 4, # 8 Exhibit 5, # 9 Exhibit 6, # 10 Exhibit 7, # 11 Exhibit 8, # 12 Exhibit 9, # 13 Exhibit 10, # 14 Exhibit 11, # 15 Exhibit 12, # 16 Exhibit 13, # 17 Exhibit 14, # 18 Exhibit 15, # 19 Exhibit 16, # 20 Exhibit 17, # 21 Exhibit 18, # 22 Exhibit 19, # 23 Exhibit 20, # 24 Exhibit 21, # 25 Exhibit 22, # 26 Exhibit 23, # 27 Exhibit 24, # 28 Exhibit 25, # 29 Exhibit 26, # 30 Exhibit 27, # 31 Exhibit 28, # 32 Exhibit 29, # 33 Exhibit 30)(Bonn, Amanda)

Exhibit 24

Informorion
Processing & Management
Printed in Great Britain.

Vol. 26, No. 6, pp. 719-738,

1990
Copyright

0

0306-4573/90 $3.00 + .@I
1990 Pergamon
Press plc

INTEGRATION OF USER PROFILES:
MODELS AND EXPERIMENTS IN
INFORMATION RETRIEVAL
SUNGH. MYAENG
4-206 Center for Science & Technology, School of Information Studies,
Syracuse University, Syracuse, NY 13244
and
ROBERT R. KORFHAGE*’
Department of Information Science, University of Pittsburgh,
Room 737 LIS Building, Pittsburgh, PA 15260
(Received 21 May 1988; accepted

in final form 26 April 1990)

Abstract-One difficult problem in information retrieval (IR) is the proper interpretation of user queries. It is extremely hard for users to express their information needs in
a specific yet exhaustive way. In an effort to alleviate this problem, two theoretical models have been proposed to utilize user characteristics maintained in the form of a user profile. Although the idea of integrating user profiles into an IR system is intuitively
appealing, and the models seem viable, no research to date has established a foundation
for the roles of user profiles in such a system. Aiming at the investigation of the roles
of user profiles, therefore, this study first identifies and extends various query/profile
interaction models to provide a ground upon which the investigation can be undertaken.
From a continuum of models characterized on the basis of interaction types, metrics, and
parameters, nearly 400 models are chosen to investigate the “model space.” New measures are developed based on the notion of user satisfaction/frustration.
In addition,
three different criteria are used to guide users in making judgments on the quality of
retrieved items. Analysis of the data obtained from the experiments shows that, for a
wide variety of criteria and metrics, there are always some query/profile interaction models that outperform the query alone model. In addition, preferable characteristics for different criteria are identified in terms of interaction types, parameters, and metrics.

1. INTRODUCTION

The problem of retrieving information from natural language databases has been studied
during the past quarter century. In traditional context, retrospective information retrieval
(IR) systems are those in which a user initiates the search process by means of a set of active queries and receives a set of references to items of potential interest.
One difficult problem in such systems is the transformation of the user’s information
need to the form of an explicit query which accurately matches the original intention, and
retrieves all items of interest in the database being searched, and only those. Therefore,
users often have great difficulty in using an IR system successfully regardless of the query
language implementation (e.g., a vector form, a boolean expression of terms, a combination of both [1,2,3], or other retrieval models [4,5,6,7,8]). As a result, user queries are not
completely satisfactory in expressing the needs in most retrieval situations. It seems natural that the output of a system based on such a query is necessarily incomplete and
unsatisfactory.
One reason underlying this query formulation problem is the mismatch between terms
used in a query and those used in documents. Blair and Maron [9] analyzed the poor performance of a large-scale IR system and contended that it is exceedingly difficult for users
to predict the exact words, word combinations, and phrases that are used by all (or most)
relevant documents and only (or primarily) by those documents. This difficulty is usually
*All editorial correspondence

should be sent to Robert R. Korfhage.
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