U.S. patent application number 13/936562 was filed with the patent office on 2013-11-07 for method and apparatus for targeting messages to users in a social network.
The applicant listed for this patent is SRI International. Invention is credited to SUGATO BASU, Jeffrey Davitz, Mark Drummond, Jiye Yu.
Application Number | 20130297714 13/936562 |
Document ID | / |
Family ID | 39940301 |
Filed Date | 2013-11-07 |
United States Patent
Application |
20130297714 |
Kind Code |
A1 |
BASU; SUGATO ; et
al. |
November 7, 2013 |
METHOD AND APPARATUS FOR TARGETING MESSAGES TO USERS IN A SOCIAL
NETWORK
Abstract
A method and apparatus for targeting messages to users in a
social network, for example by first identifying topics in the
social network is provided. One embodiment of a method for
discovering topics in a social network includes collecting
information from the social network, the information including at
least one of: interactions between users of the social network or
profile information for the users, determining a global topic model
including at least one topic, based on the collected information,
and locally refining the global topic model in accordance with the
collected information.
Inventors: |
BASU; SUGATO; (Redwood City,
CA) ; Yu; Jiye; (Menlo Park, CA) ; Davitz;
Jeffrey; (Danville, CA) ; Drummond; Mark; (Los
Altos Hills, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SRI International |
Menlo Park |
CA |
US |
|
|
Family ID: |
39940301 |
Appl. No.: |
13/936562 |
Filed: |
July 8, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12002412 |
Dec 17, 2007 |
8484083 |
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13936562 |
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60887778 |
Feb 1, 2007 |
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60892824 |
Mar 2, 2007 |
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Current U.S.
Class: |
709/206 |
Current CPC
Class: |
H04L 51/32 20130101;
G06Q 30/02 20130101; H04L 12/1859 20130101 |
Class at
Publication: |
709/206 |
International
Class: |
H04L 12/58 20060101
H04L012/58 |
Goverment Interests
REFERENCE TO GOVERNMENT FUNDING
[0002] This invention was made in part with Government support
under contract number NBCHD030010, awarded by the Department of the
Interior National Business Center. The Government has certain
rights in this invention.
Claims
1. A method for grouping users of a computer network, the method
comprising: algorithmically analyzing content of a first set of
observed interactions among the users; automatically inferring a
topic, based at least in part on the algorithmically analyzing;
associating a subset of the users with the topic; and determining a
relative strength of a relationship between a first user and a
second user in the subset of the users, wherein the relative
strength is based at least in part on a quantitative measure of a
second set of observed interactions involving the first user and
the second user.
2. The method of claim 1, wherein the first set of observed
interactions includes electronic mail.
3. The method of claim 1, wherein the first set of observed
interactions includes an instant message.
4. The method of claim 1, wherein the first set of observed
interactions includes a posting to a web site, blog, or online
forum.
5. The method of claim 1, wherein the first set of observed
interactions includes a comment or tag made on a web site or
blog.
6. The method of claim 1, wherein the algorithmically analyzing
comprises: extracting a first word from the first set of observed
interactions; generating an initial topic by associating the first
word with a second word; associating at least some of the users
with the initial topic, based at least in part on a similarity
measure that relates the initial topic to profile information for
the at least some of the users.
7. The method of claim 6, wherein the automatically inferring
comprises: calculating a centroid of the profile information,
wherein the centroid is the topic.
8. The method of claim 6, wherein the automatically inferring
comprises: merging the initial topic with another topic.
9. The method of claim 1, further comprising: refining the topic
based, at least in part, on the first set of observed
interactions.
10. The method of claim 9, wherein the refining comprises:
filtering the first set of observed interactions according to a
criterion related to the topic; adjusting a weight of a link
relating a pair of interactions in the first set of observed
interactions, in accordance with a result of the filtering; and
indicating the weight in a model of the topic.
11. The method of claim 10, wherein the criterion is a frequency of
appearance in the first set of observed interactions of a word
related to the topic.
12. The method of claim 11, wherein the adjusting comprises:
setting the weight to a value that is proportional to the
frequency.
13. The method of claim 10, further comprising: dividing the topic
into a plurality of sub-topics, based at least in part on the
link.
14. The method of claim 1, wherein the automatically inferring
comprises: mapping the word into an ontology to produce a
normalized set of concepts that defines the topic.
15. The method of claim 14, wherein the ontology is
community-generated.
16. The method of claim 1, further comprising: obtaining a second
third set of observed interactions among the users; and dynamically
updating the topic, based at least in part on the third set of
observed interactions.
17. The method of claim 16, wherein the dynamically updating is
performed incrementally.
18. The method of claim 17, wherein the dynamically updating
comprises: filtering the third set of observed interactions
according to a criterion related to the topic; adjusting a weight
of a link relating a pair of interactions in a cumulative set of
interactions that includes the first set of interactions, the
second set of interactions, and the third set of interactions, in
accordance with a result of the filtering; and indicating the
weight in a model of the topic.
19. The method of claim 16, wherein the dynamically updating
comprises adjusting the relative strength.
20. A computer-readable storage device having stored thereon a
plurality of instructions, the plurality of instructions including
instructions which, when executed by a processor, cause the
processor to perform a method for grouping users of a computer
network, comprising: algorithmically analyzing content of a first
set of observed interactions among the users; automatically
inferring a topic, based at least in part on the algorithmically
analyzing; associating a subset of the users with the topic; and
determining a relative strength of a relationship between a first
user and a second user in the subset of the users, wherein the
relative strength is based at least in part on a quantitative
measure of a second set of observed interactions involving the
first user and the second user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of co-pending U.S. patent
application Ser. No. 12/002,412, filed Dec. 17, 2007, which in turn
claims the benefit of U.S. Provisional Patent Applications Ser. No.
60/887,778, filed Feb. 1, 2007; and Ser. No. 60/892,824, filed Mar.
2, 2007. All of these applications are herein incorporated by
reference in their entireties.
COMPUTER PROGRAM LISTING APPENDIX
[0003] A computer program listing illustrating source code for an
exemplary embodiment of the present invention is provided herewith
as Appendix I through Appendix XIII, which is herein incorporated
by reference in its entirety.
COPYRIGHT PROTECTION
[0004] A portion of the disclosure of this patent document contains
material which is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent file or records, but otherwise
reserves all copyright rights whatsoever.
FIELD OF THE INVENTION
[0005] The present invention relates generally to the field of
computer networking, and relates more specifically to the field of
targeted messaging to users of network communications.
SUMMARY OF THE INVENTION
[0006] A method and apparatus for targeting messages to users in a
social network, for example by first identifying topics in the
social network is provided. One embodiment of a method for
discovering topics in a social network includes collecting
information from the social network, the information including at
least one of: interactions between users of the social network or
profile information for the users, determining a global topic model
including at least one topic, based on the collected information,
and locally refining the global topic model in accordance with the
collected information.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The teachings of the present invention can be readily
understood by considering the following detailed description in
conjunction with the accompanying drawings, in which:
[0008] FIG. 1 illustrates a flow diagram that depicts one
embodiment of a method for determining groups of users and topics,
according to the present invention;
[0009] FIG. 2 illustrates a flow diagram that depicts one
embodiment of a method for targeting messages, according to the
present invention; and
[0010] FIG. 3 is a high-level block diagram of the present method
for targeting messages that is implemented using a general-purpose
computing device.
[0011] To facilitate understanding, identical reference numerals
have been used, where possible, to designate identical elements
that are common to the figures.
DETAILED DESCRIPTION
[0012] The present invention relates to a method and apparatus for
targeting messages to users in a social network. Although
embodiments of the invention are described in the context of
advertisement distribution, it is to be appreciated that the
present invention may be applied to the distribution of any type of
message, including non-commercial messages such as recommendations
(e.g., restaurants, movies, music, news articles, web pages, or the
like) that a user or group of users might like based on their
interactions.
[0013] FIG. 1 illustrates a flow diagram that depicts one
embodiment of a method 100 for determining groups of network users
and topics, according to the present invention. For instance, the
method 100 may be implemented to extract groups and topics from
interactions observed in a social network. For the purposes of
certain steps of the method 100, the network is represented as a
graph in which the nodes represent network users and edges
represent the relationships or connectivity among the nodes (e.g.,
explicit relationships such as "friend" or implicit relationships
such as those formed through reference or comment).
[0014] The method 100 is initialized at step 105 and proceeds to
step 110, where the method 100 collects information from the
network. In one embodiment, the collected information includes:
interactions between a plurality of network users in the network
and profile information about the network users. In one embodiment,
interactions collected include at least one of: a piece of
electronic mail, an instant message, a posting to a website or to a
blog, a comment or a tag made on a website or blog, and an online
forum discussion posting. In one embodiment, the collected profile
information includes: data about a network user that was posted
(e.g., to a web site) by the network user, data provided by the
network user as part of a registration process (e.g., published or
unpublished information provided to a website for purposes of
setting up a posting account), and data collected about the network
user from other sources (e.g., other network users, other web
sites, public records). Profile information includes both free-form
text and categorized or typed information (e.g., demographic
information such as age, location, gender).
[0015] In one embodiment, a data sampling approach is employed in
accordance with step 110. In this case, a subset of interactions
and/or network users is monitored (as opposed to collecting all
interactions between all network users, and collecting profile
information of every network user). In one embodiment, the subset
of interactions is randomly selected.
[0016] In step 115, the method 100 extracts words and sequences of
words (n-grams) contained in the collected interactions. In one
embodiment, the words and n-grams are mapped into an ontology,
e.g., to determine a normalized set of concepts, as described later
herein with respect to FIG. 2.
[0017] In another embodiment, field-specific attributes are
extracted from the profile information. For example, data relating
to "music," "movies," "books," "age," or the like may be extracted.
In a further embodiment, as part of the extraction step 115, a
classifier examines the collected interactions with respect to the
extracted field-specific features to obtain weights of the
field-specific features relative to links between the interactions.
The resultant weights provide a measure of how discriminative a
particular profile attribute type is in predicting interaction
links. In an alternative embodiment, these weights are
predetermined rather than calculated by a classifier.
[0018] In optional step 120 (illustrated in phantom), the method
100 generates a set of global soft constraints with associated
penalties. The global soft constraints are used to bias subsequent
global topic model searches (described in further detail below with
respect to step 130) toward a desired solution. In other words, a
subsequent search attempts to optimize for the lowest penalty
provided by the global soft constraints. In one embodiment, global
soft constraints are generated from a set of initial rules or
assumptions over all sets of users. For example, an initial rule
could dictate that users who share similar musical tastes form a
group, or that users of the same age living in the same locality
form a group. In a further example, an initial rule could describe
an assumption that a particular set of words describes a topic.
[0019] In step 130, the method 100 determines a global set of
topics, based on the profile information collected in step 110. In
one embodiment, the global set of topics is determined in
accordance with a generalization of the spherical KMeans algorithm
described in "Concept Decompositions for Large Sparse Text Data
using Clustering," by I. S. Dhillon and D. S. Modha in Machine
Learning, vol. 42:1, pp. 143-175, January 2001, which is
incorporated herein by reference.
[0020] In this embodiment, an iterative algorithm that starts with
an initial topic assumption is used to determine the global set of
topics, where a topic comprises a group of words and phrases that
are considered related to the same concept. In order to determine
the set of global topics in accordance with step 130, the method
100 first creates a group of people (e.g., network users), for
example by performing clustering based on a similarity measure
(such as cosine similarity) of normalized feature vectors
constructed from the profile information to the current (initial)
topics. For typed profile information, the information types are
taken into account by creating a composite feature vector that
combines the feature vectors of each information type, performing
normalization, and considering a weighted combination of the
similarities across different information types in the
clustering.
[0021] For each group created, the method 100 next infers the topic
of the group by calculating the centroid of the feature vectors of
the group. The inferred topics may, in turn, be used in place of
the initial topic assumption to create new groups. In one
embodiment, iterations of group creation and topic inference are
continued until the difference between successive estimates of an
objective function (calculated using the inferred topics) is less
than a predetermined threshold. In alternative embodiments, other
convergence criteria, such as an iteration counter (where iteration
is deemed complete after a predetermined maximum number of
iterations have been performed), are used to determine how many
iterations are necessary.
[0022] Once the clustering iterations are complete, the method 100
performs post-processing on the output (inferred topics) to merge
similar topics, for example using complete-link hierarchical
clustering based on cosine similarity. The post-processing outputs
a reduced set of merged global topics, with each person (e.g.,
network user) being assigned to a single topic. In an alternative
embodiment (where an expectation maximization (EM) algorithm is
used instead of KMeans-type assignment in the determination of the
global set of topics), a user may be probabilistically assigned to
multiple topics, and a probabilistic merging is performed in the
post-processing step. One exemplary EM-type algorithm that may be
advantageously implemented in accordance with step 130 is described
in "Maximum likelihood from incomplete data via the EM algorithm,"
by Dempster, A. P., Laird, N. M., and Rubin, D. B. in the Journal
of the Royal Statistical Society, B, 39, 1-38, 1977, which is
incorporated herein by reference.
[0023] Once the global topic model is determined, the method 100
proceeds to step 140 and performs local refinement for each topic,
using the collected interaction data. For each topic output by step
130, the method 100 determines a connection network among the
members of the group from which the topic was inferred, to
determine a relative "strength" of relationships among the users in
the network (e.g., by considering the number of interactions).
[0024] For each topic, the collected interactions are filtered
according to the given topic (e.g., the frequency of the topic
words and/or phrases appearing in the interactions, where the
weight of a link after the filtering is proportional to the
projection of the words contained in the interaction on the topic
under consideration). Partitions of the graph representing the
network are created based on the link weights and the graph
topology, for example by using a graph clustering algorithm that
performs model selection (i.e., automatically selects the right
number of graph clusters), such as the Markov Cluster (MCL)
algorithm described by Stijn van Dongen in "Graph Clustering by
Flow Simulation," Ph.D. thesis, University of Utrecht, May 2000,
which is incorporated herein by reference.
[0025] The method 100 then computes sub-topics within each global
topic, by using the iterative clustering algorithm described above
with respect to step 130, but also including the links within each
graph partition as additional constraints (e.g., by using a
semi-supervised clustering model like the Hidden Markov Random
Field (HMRF) described in "A Probabilistic Framework for
Semi-Supervised Clustering," by Sugato Basu, Mikhail Bilenko, and
Raymond J. Mooney in the Proceedings of the 10th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining
(KDD-2004), Seattle, Wash., August 2004, which is incorporated
herein by reference). In the cluster assignment step, the method
100 may use either a fast (but less accurate) greedy intracluster
medium (ICM) algorithm for inference, or a slower (but more
accurate) message passing algorithm using loopy belief
propagation--this allows a trade-off between the efficiency of the
inference algorithm and the quality of the final result, as
discussed, for example, in, "A Comparison of Inference Techniques
for Semi supervised Clustering with Hidden Markov Random Fields,"
by Mikhail Bilenko, and Sugato Basu in Proceedings of the ICML-2004
Workshop on Statistical Relational Learning and its Connections to
Other Fields (SRL-2004), Banff, Canada, July 2004, which is
incorporated herein by reference.
[0026] The method 100 then filters the refined clustering results
by: (i) rank ordering the topics in terms of their quality (cluster
coherence); and (ii) rank ordering the words and/or phrases in the
topic using a score comprising the feature weights in the topic
centroids and mutual information of the features with respect to
the cluster partitioning. The filtered clusters thereby provide an
improved set of descriptive and discriminative words for a
topic.
[0027] In optional step 150 (illustrated in phantom), the method
100 dynamically updates the clusters of topics and users as more
information becomes available from the social network (e.g., in
terms of additional collected interactions between the users,
additional profile information, and the addition of new users to
the social network). In one embodiment, the global topics and user
groups are incrementally updated, using a hybrid algorithm that
interleaves online clustering on incremental data streams with
periodic offline clustering on batch data, for example as described
in "Topic Models over Text Streams: A Study of Batch and Online
Unsupervised Learning." by Arindam Banerjee, Sugato Basu in
Proceedings of the SIAM International Conference on Data Mining
(SDM-2007), Minneapolis, Minn., April 2007, which is incorporated
herein by reference. The local refinement algorithm implemented in
accordance with step 140 is then periodically performed again, when
the number of links added/deleted for a user's collected
interactions crosses a predetermined threshold, such threshold
being provided as an input to the algorithm.
[0028] The method 100 terminates in step 155.
[0029] FIG. 2 illustrates a flow diagram that depicts one
embodiment of a method 200 for targeting messages, according to the
present invention. The method 200 may be implemented, for example,
by an advertiser wishing to distribute an advertising message to a
group of consumers (e.g., social network users) who are most likely
to be interested in the advertised product or service.
[0030] The method 200 is initialized at step 205 and proceeds to
step 210, where the method 200 receives from a user (e.g., an
advertiser) a set of one or more terms (keywords) representing a
proposed item of advertising (e.g., words descriptive of a
product). The terms can include phrases (i.e., groups of words) and
independent words, and may be a single word.
[0031] At step 220, the terms received in step 210 are projected
into at least one ontology source, to determine a normalized set of
concepts represented by the received terms. An ontology source in
this context represents a data source that describes the
relationships of particular terms to concepts (e.g., the words used
to describe a particular concept in an encyclopedia), and may
further relate the described concepts to one another. Exemplary
ontology sources that can be used for this purpose include
community-generated content such as general encyclopedias (e.g.,
Wikipedia.RTM.), directories (e.g., the Open Directory Project),
and topic-specific encyclopedias (e.g., the Internet Movie
Database). Domain-specific ontologies and/or dictionaries can also
be used as ontology sources, whether general or topic-specific
(e.g., medical dictionaries and legal dictionaries).
[0032] As discussed above, the ontology source(s) into which the
terms received in step 210 are projected may include one or more
community-generated ontology sources. Community-generated ontology
sources are typically the result of iteration, modification, and
refinement by a group of community members, such that the resulting
data represents a consensus of the community on the meanings and
relationships of the particular represented terms, topics, and
concepts. As such, community-generated ontology sources may
comprise a valuable resource within the context of the method 200,
where a goal is to normalize the user's terms or keywords in light
of what the terms mean to the target community. A
community-generated source that was made by the target community
may include some of the richest data for use in determining how
members of the target community are likely to interpret messages
the user targets to them.
[0033] In one embodiment, the method 200 performs a separate
projection of the user's terms into each target ontology source.
The projections are presented to the user in order to give the user
a feel for the type of probes that will be subsequently used to
analyze on-line conversational content. In this context, "probe"
refers to the use of the projections into the ontology sources as
patterns to explore the space of network user-generated
content.
[0034] At step 230, the method 200 receives a selection of probes
from the user. The probes selected comprise the user's set of
desired probes, selected from the one or more of the projections
presented in step 220. As discussed with respect to step 220, the
probes are used as initial patterns for exploring the space of
network user-generated content.
[0035] At step 240, the method 200 maps the probes selected by the
user in step 230 into a set of data representing interactions
between network users (e.g., potential advertising targets). For
instance, the selected probes may be mapped into the social network
structures of network user-generated content web sites. This
mapping yields clusters of messaging targets, namely, the people
(e.g., network users) who are associated (with a relatively high
probability) with the probed topics and hence are more valuable
messaging targets (e.g., more likely to purchase the products
depicted in an advertising message). The clusters that are
developed in this step will typically be refinements of the topics
that are generated from the initial projection in step 220, and
will present the topic of the cluster (e.g., what the users within
the cluster are discussing), information about the size of the
cluster (e.g., the number of members and participation strength),
and metrics about the interactions within the cluster (e.g., the
frequency of interactions and temporal pattern). In one embodiment,
the method 100 described earlier herein with respect to FIG. 1 is
used to perform the mapping in accordance with step 240, with the
probes forming an initial rule that will bias the topic model. The
probes may also be used to post-process the topic words and/or
phrases, in order to generate relevant categorical descriptors.
[0036] In step 245, the method 200 determines whether to modify the
messaging targets. For example, the user may wish to change or
modify the selection of messaging targets based the refinement
information generated in step 240 (e.g., because of the discovered
content or because of other information about the users associated
with sub-topics). If the method 200 concludes in step 245 that the
user does wish to modify the messaging targets, the method 200
returns to either step 210 (to receive new terms) or to step 230
(to receive a new selection of probes), depending on the user's
selection, and proceeds as described above.
[0037] Alternatively, if the method 200 concludes in step 245 that
the user does not wish to modify the messaging targets, the method
200 proceeds to step 250 and receives a segment selection from the
user. The user selects desired target segments from the clusters
presented in step 240, the selected segments being those associated
with topic refinements that are of interest to the user. A segment
for the purposes of the present invention may comprise an entire
cluster or set of clusters, or filtered portions of one or more
clusters (e.g., a credit card company may select a segment that
comprises only those network users in the presented clusters who
have a threshold credit rating). For example, consider a user that
has indicated, through his or her initial selection, criteria
concepts that match the concept/topic "football" in one of the
ontology sources used in step 220. The social network-based
clustering could then yield topic refinements (what users in the
social network are discussing) such as "buying tickets for football
games", "fantasy football leagues", and the like. Thus, in step
250, the user can select the messaging targets most likely to be in
his or her message, for example football ticket buyers as opposed
to fantasy football participants.
[0038] In step 260, the method 200 collects a message (e.g., a set
of advertising copy) from the user. The message is then transmitted
to the target segments identified in step 250 (e.g., the users
participating in the social network conversations regarding the
selected topic) through a network fulfillment process.
[0039] In optional step 270 (illustrated in phantom), the method
200 monitors the performance of the message provided to the
messaging targets. This step may be useful, for example, where the
message comprises advertising content. In some embodiments, the
method 200 presents performance statistics to the user based on
refined topic segmentation.
[0040] The method 200 terminates in step 275.
[0041] In some embodiments of method 200, steps 210 and 220 may be
optional. For instance, the user may decide not to provide a set of
representative keywords, rather to browse an ontology source (e.g.,
concept directory) directly to select target concepts that will
guide a particular message placement. For example, without
providing any keywords, but having selected Wikipedia.RTM. as the
target ontology source, the user can chose the top-level
Wikipedia.RTM. category of "Religion and belief systems" and the
single associated sub-category of "Confucianism". In this way,
without providing any keywords, the user has indicated the
particular concept that should be used to guide the placement of a
message. In this alternative (i.e., keyword-free) embodiment, steps
240, 250, 260, and 270 are executed in substantially the same
manner as described above.
[0042] Embodiments of the present invention may be advantageously
applied to the field of advertising, where an advertiser user may
be enabled to build an advertising campaign incrementally, by first
selecting target concepts and then monitoring how those concepts
are active in user-generated data sources. The typical types of
user-generated data sources that are examined are conversations,
for example those that take place in so-called "social media" web
sites, where users create web pages that contain text comments to
others in the community.
[0043] Embodiments of the present invention thus allow an
advertiser user to see relevant statistics about the community
activity level associated with any given concept, for instance, in
terms of audience size, posting frequency, and other communication
intensity measures.
[0044] FIG. 3 is a high-level block diagram of the message
targeting method that is implemented using a general purpose
computing device 300. In one embodiment, a general purpose
computing device 300 comprises a processor 302, a memory 304, a
message targeting module 305 and various input/output (I/O) devices
306 such as a display, a keyboard, a mouse, a stylus, a wireless
network access card, and the like. In one embodiment, at least one
I/O device is a storage device (e.g., a disk drive, an optical disk
drive, a floppy disk drive). It should be understood that the
message targeting module 305 can be implemented as a physical
device or subsystem that is coupled to a processor through a
communication channel.
[0045] Alternatively, the message targeting module 305 can be
represented by one or more software applications (or even a
combination of software and hardware, e.g., using Application
Specific Integrated Circuits (ASIC)), where the software is loaded
from a storage medium (e.g., I/O devices 306) and operated by the
processor 302 in the memory 304 of the general purpose computing
device 300. Thus, in one embodiment, the message targeting module
305 for monitoring and analyzing user communications, and targeting
messages based thereon, as described herein with reference to the
preceding Figures can be stored on a computer readable medium or
carrier (e.g., RAM, magnetic or optical drive or diskette, and the
like).
[0046] It should be noted that although not explicitly specified,
one or more steps of the methods described herein may include a
storing, displaying and/or outputting step as required for a
particular application. In other words, any data, records, fields,
and/or intermediate results discussed in the methods can be stored,
displayed, and/or outputted to another device as required for a
particular application. Furthermore, steps or blocks in the
accompanying Figures that recite a determining operation or involve
a decision, do not necessarily require that both branches of the
determining operation be practiced. In other words, one of the
branches of the determining operation can be deemed as an optional
step.
[0047] Although various embodiments which incorporate the teachings
of the present invention have been shown and described in detail
herein, those skilled in the art can readily devise many other
varied embodiments that still incorporate these teachings.
* * * * *