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 25

TOWARDS AN INTELLIGENT AND PERSONALIZED RETRIEVAL SYSTEM
Sung H. Myaeng and Robert R. Korfhage
Department of Computer Science and Engineering
Southern Methodist University

ABSTRACT. Development of an information retrieval system that can be personalized to each user requires maintaining and continually updating an interest profile for
each individual user. Since people tend to be poor at self-description, it is suggested
that profile development and maintenance is an area in which machine learning and
knowledge base techniques can be profitably employed. This paper presents a model
for such an application of AI techniques.
KEYWORDS: information retrieval, user profile, learning, personalized system
1. Introduction
In the context of conventional information retrieval systems (IRS), the search process
is initiated and completed by a set of queries from a user. Each query, usually in the
form of a vector or Boolean expression, consists of a set of key terms to be matched
with the contents of relevant items. To improve the retrieval effectiveness,
modification of the user query through the application of user feedback has been studied with some successful results [13].
There have also been systems, called selective dissemination of information systems
(SDI), that selectively distribute incoming information to appropriate users based on
user interest profile. However, only recently has a set of models been proposed that
effectively combines the two different modes of the systems, thereby attempting to
enhance the quality of retrieved items [3,8,9].
One of the major stumbling blocks in the conventional IRS is the problem of formulating a query which accurately matches the user's needs and the contents of potentially relevant items[ 1,12]. Unfortunately, different users expect different sets of items
from the same query and make different relevance judgements on the same retrieved
items, directly related to their individual needs. But the conventional retrieval system
disregards the individual user's characteristics and the fact that diverse users have
different perceptions of the underlying system. While it is natural that a user perceives
the system in the light of his or her experience and needs, both the restricted structure of a query and the nature of the conventional system itself make this perception
unavailable to the system. We believe that knowledge captured in a user profile
e m b e d d e d in the system will play an essential role in making a personalized system.
One effect can be to retrieve a broader range of items, some of which would never be
brought to the user's attention on the basis of the query alone. People prefer a
librarian who can surprisingly provide information not explicitly requested but judged
to be important to them. Profile information will also help the system tailor the
retrieved items to a particular user's needs and rank them appropriately. Again, a
friendly and intelligent librarian can eliminate some information which is not of the
user's concern but would have been retrieved by a novice librarian who had to rely

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