U.S. patent application number 09/169029 was filed with the patent office on 2001-12-06 for content based method for product-peer filtering.
Invention is credited to AGGARWAL, CHARU C., YU, PHILIP S..
Application Number | 20010049623 09/169029 |
Document ID | / |
Family ID | 22613989 |
Filed Date | 2001-12-06 |
United States Patent
Application |
20010049623 |
Kind Code |
A1 |
AGGARWAL, CHARU C. ; et
al. |
December 6, 2001 |
CONTENT BASED METHOD FOR PRODUCT-PEER FILTERING
Abstract
The present invention derives product characterizations for
products offered at an e-commerce site based on the text
descriptions of the products provided at the site. A customer
characterization is generated for any customer browsing the
e-commerce site. The characterizations include an aggregation of
derived product characterizations associated with products bought
and/or browsed by that customer. A peer group is formed by
clustering customers having similar customer characterizations.
Recommendations are then made to a customer based on the processed
characterization and peer group data.
Inventors: |
AGGARWAL, CHARU C.;
(YORKTOWN HEIGHTS, NY) ; YU, PHILIP S.;
(CHAPPAQUA, NY) |
Correspondence
Address: |
FRANK CHAU
F CHAU AND ASSOCIATES
1900 HEMPSTEAD TURNPIKE
SUITE 501
EAST MEADOW
NY
11554
|
Family ID: |
22613989 |
Appl. No.: |
09/169029 |
Filed: |
October 9, 1998 |
Current U.S.
Class: |
705/14.51 |
Current CPC
Class: |
G06Q 30/0631 20130101;
G06Q 30/02 20130101; Y10S 707/99943 20130101; G06Q 30/0253
20130101; G06Q 30/0625 20130101; Y10S 707/99933 20130101 |
Class at
Publication: |
705/14 |
International
Class: |
G06F 017/60 |
Claims
What is claimed is:
1. A method for providing product recommendations to customers in
an e-commerce environment, comprising the steps of: (a) deriving
product characterizations for each of said plurality of products;
(b) creating individual customer characterizations on each of said
customers based on usage of said product characterizations by each
of said respective customers; (c) clustering based on similarities
in said customer characterizations, to form peer groups; (d)
categorizing individual customers into one of said peer groups; and
(e) making product recommendations to customers based on said
customer characterizations and information from said categorized
peer groups.
2. The method according to claim 1, wherein the step of creating
customer characterizations includes extracting product
characterizations when said customers browse or purchase said
products.
3. The method according to claim 27 wherein the step of creating
customer characterizations further includes concatenating each of
the product characterizations of all products browsed or purchased
by an individual customer.
4. The method according to claim 1, wherein the step of deriving
product characterizations includes use of text characterizations
associated with said plurality of products.
5. The method according to claim 1, wherein said step of creating
product characterizations further includes the step of: (a) finding
a frequency of occurrence for each word in said text descriptions;
(b) dividing the total frequency for each word by the frequency of
occurrence for said word for all customers; (c) finding the
standard deviation for each word; (d) selecting words having larger
standard deviations; and (e) expressing a product characterization
based on said selected words.
6. The method of claim 1, further including the steps of: (a)
deriving product characterizations based on a current on-line
session; (b) accessing historical product characterizations from
memory; (c) creating a customer characterization by weighted
concatenating characterizations from steps (a) and (b); (d)
computing a cluster centroid for each of said peer groups; (e)
selecting the peer groups whose cluster centroid is closest to the
characterization created in step (c); and (f) generating one of
product, peer and profile recommendations based on said selected
peer group.
7. The method of claim 6, wherein said cluster centroid is computed
by concatenating text characterizations of all customer
characterizations in each peer group.
8. The method of claim 6, wherein the recommendations comprise a
weighted concatenation of text-characterizations of products bought
and browsed in said current on-line session.
9. A stored program device readable by a computer, tangibly
embodying a program of instructions executable by the computer to
perform process steps for providing product recommendations to
customers in an e-commerce environment, comprising the steps of:
accessing product information on products offered for sale on an
e-commerce site; deriving product characterizations from said
product information; accessing customer usage information relating
to said products; creating customer characterization on each of
said customers based on said accessed usage information; clustering
based on similarities in said customer characterizations, to form a
plurality of peer groups; receiving queries from a current session
customer; creating present customer characterizations associated
with said current session customer; categorizing said current
session customer into a selected peer group based on similarities
in the present customer characterizations and past customer
characterizations; responding to said queries with product
recommendations to said current session customer based at least on
information from said categorized selected peer group.
10. The device according to claim 9, further including stored
programs for causing said computer to extract data exchanged
between said current session customer and said site to create
present customer characterizations.
11. The device according to claim 9, wherein the step of creating
customer present characterizations further includes weighted
concatenating each of the product characterizations of all products
browsed or purchased by said present session customer.
12. The device of claim 9, further including stored programs for
causing said computer to: compute a cluster centroid for each of
said peer groups by concatenating text characterizations of all
customer characterizations in each peer group; select the peer
group having a cluster centroid closest to the present customer
characterizations; and generate one of product, peer and profile
recommendations based on data from said selected peer group.
13. In a computer having a processor and stored program for causing
the computer to interact with customers in an e-commerce
environment and to provide answers to customer inquiries, said
stored program comprising: means for deriving product
characterizations for a plurality of products; means for creating
customer characterizations based on usage of said product
characterizations by said customers; means for clustering, based on
similarities in said customer characterizations, for forming a
plurality of peer groups; means for placing individual customers
into one of said peer groups; and means for providing answers to
said customer queries based on said customer characterizations and
information from said peer groups.
14. The stored program of claim 13, wherein said means for creating
customer characterizations includes storage for archiving said
customer characterizations.
15. The stored program of claim 13, further including means for
creating present customer characterizations for a currently on-line
customer based on a concatenation of data extracted from usage of
product characterizations by the currently on-line customer.
16. The stored program of claim 15, further including means for
placing a currently on-line customer in one of said peer groups
based on similarities in said present customer characterizations
and stored characterizations.
17. The stored program of claim 13, wherein said means for deriving
product characterizations include means for extracting text
descriptions associated with said products.
18. The stored program of claim 13, wherein said means for
providing answers includes means for recommending products to an
individual customer based on product characterizations from
products browsed by said individual customer in a current
session.
19. The stored program of claim 13, wherein said means for
providing answers includes means for recommending products to an
individual customer based on a weighted frequency of historic
product characterizations and a weighted frequency of present
product characterizations from products browsed by said individual
customer in a current session.
20. The stored program of claim 13, wherein said means for
providing answers includes means for recommending products to an
individual customer based on historic product
characterizations.
21. The stored program of claim 13, wherein said means for
providing answers includes means for recommending products to an
individual customer based on products in a promotion list.
22. The stored program of claim 13, wherein said means for
providing answers includes providing a peer distribution profile
based on product frequency for one of said peer groups.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of Invention
[0002] The present invention relates to an automated computer based
apparatus and method for making product recommendations over an
electronic commerce network. More particularly, the invention is
directed to an apparatus and method for making product
recommendations based on customer user behavior.
[0003] 2. Discussion of Related Art
[0004] With the recent increase in popularity of on-line shopping
over the Internet, entities providing the shopping sites are
interested in obtaining information on shoppers that would help in
selling their products. The traditional customer or market surveys
used in obtaining such information are applicable and usable for
the providers. Companies such as Likeminds, Inc.
(www.likeminds.com) and Firefly Network, Inc. (www.firefly.com)
provide survey information which are based on explicit ratings by
customers, commonly referred to in the art as recommendation
engines using `collaborative filtering`. The use of engines based
on ratings have particular applicability in products which are
uniform and of a particular type. For example, in the case of
Likeminds, customers are asked to provide ratings for preferences
for products such as compact discs, based on their degrees of likes
or dislikes. These ratings are then collected and archived for
later use. At some point in the future a product recommendation
will be made to a new customer based on the previously archived
data of other customers. Recommendations made based on past
explicit ratings by customers are known as `collaborative
filtering`.
[0005] The collaborative filtering approach does not work well if
the customers do not participate in the explicit ratings of
products. A customer or purchaser in an e-commerce environment
typically prefers to minimize his time on-line and is usually
unwilling to spend extra time, especially in rating products,
on-line or otherwise.
[0006] Other companies offer `content-based` filtering which uses
extracted texts and information from e-commerce websites.
[0007] An example is the intelligent infrastructure offered by
Autonomy, Inc. (www.autonomy.com) . This system provides an
Agentware content server, which is a scaleable content
personalization and organization engine for Internet information
providers. This technique extracts key concepts from documents and
websites to automate the categorization, cross-referencing,
hyperlinking, and presentation of the information. The customer
profiling system of this software enables information and service
providers to understand the interests of customers and deliver
personalized information.
[0008] Another company which provides intelligent servers is Aptex
Software (www.aptex.com). Aptex uses a `Content Mining` method
which automatically analyzes text and other unstructured content to
make intelligent decisions and recommendations.
[0009] Net Perceptions is still another company
(www.netperceptions.com) which uses (Grouplens) implicit or
explicit ratings of products to provide recommendations. Implicit
ratings refer to the set of products bought or browsed by a
customer.
[0010] Despite the provision and availability of the above
described intelligent servers, a need still exists for a method or
system for facilitating characterization of customers and products
on the basis of customers' natural browsing/purchasing behavior,
without resorting to explicit group product ratings, and providing
recommendations based on peer group categorization, affording
substantial customer personalization to the product recommendation
process.
SUMMARY OF THE INVENTION
[0011] It is therefore an object of the present invention to
provide a system and a method of using characterizations of
products and user behavior, including user browsing or purchasing
behavior, to generate product recommendations at an e-commerce
site.
[0012] It is another object of the present invention to utilize the
characterizations to create peer groups to personalize the
recommendation to users. A peer group is a collection of customers
whose product preferences have been previously archived and whom
display a pattern of product preferences similar to that of the new
customer.
[0013] The above objectives are accomplished by a method according
to the present invention, which provides product recommendations to
customers in an e-commerce environment, comprising the steps of:
deriving product characterizations from each of a plurality of
products; creating individual customer characterizations on each of
the customers based on usage of the product characterizations by
each of the respective customers; clustering based on similarities
in the customer characterizations, to form peer groups;
categorizing individual customers into one of the peer groups; and
making product recommendations to customers based on the customer
characterizations and the categorized peer groups.
[0014] The step of creating customer characterizations preferably
includes extracting product characterizations when the customers
browse or purchase the products, and the step of creating customer
characterizations may include concatenating each of the product
characterizations of all products browsed or purchased by an
individual customer.
[0015] Preferably, the product characterizations are derived from
text characterizations of each of the products and may further
include the steps of: finding the frequency of occurrence for each
word in the text descriptions; dividing the total frequency for
each word by the frequency of occurrence for the word for all
customers; finding the standard deviation for each word; selecting
words having larger standard deviations; and expressing a product
characterization based on the selected word.
[0016] Another method of the invention preferably include the steps
of: (a) deriving product characterizations based on a current
on-line session; (b) accessing historical product characterizations
from memory; (c) creating a customer characterization by weighted
concatenating characterizations from steps (a) and (b); (d)
computing a cluster centroid for each of the peer groups; (e)
selecting the peer groups whose cluster centroid is closest to the
characterization created in step (c); and (f) generating one of
product, peer and profile recommendations based on the selected
peer group. The cluster centroid may be computed by concatenating
text characterizations of all customer characterizations in each
peer group, and the recommendations may comprise a weighted
concatenation of text-characterizations of products bought and
browsed in the current on-line session.
[0017] A system according to the present invention provides, a
computer having a processor and stored program for causing the
computer to interact with customers in an e-commerce environment
and to provide answers to customer inquiries, the stored program
comprises: means for deriving product characterizations for a
plurality of products, means for creating customer
characterizations based on usage of the product characterizations
by the customers, means for clustering, based on similarities in
the customer characterizations, for forming a plurality of peer
groups, means for placing individual customers into one of the peer
groups, and means for providing answers to the customer queries
based on the customer characterizations and information from the
peer groups.
[0018] The system preferably includes storage for archiving the
customer characterizations.
[0019] The stored program further includes means for creating
present customer characterizations for a currently on-line customer
based on a concatenation of data extracted from usage of product
characterizations by the currently on-line customer.
[0020] The stored program further includes means for placing a
currently on-line customer in one of the peer groups based on
similarities in the present customer characterizations and stored
characterizations.
[0021] The means for deriving product characterizations preferably
includes means for extracting text descriptions associated with the
products.
[0022] The means for providing answers preferably includes means
for recommending products to an individual customer based on
product characterizations from products browsed by the individual
customer in a current session.
[0023] The means for providing answers preferably includes means
for recommending products to an individual customer based on a
weighted frequency of historic product characterizations and a
weighted frequency of present product characterizations from
products browsed by the individual customer in a current
session.
[0024] The means for providing answers also preferably includes
means for recommending products to an individual customer based on
historic product characterizations.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIG. 1 is a block diagram of the illustrative system
according to the present invention.
[0026] FIG. 2 is a flow diagram illustrating an example of an
overall operation of the system according to the present
invention.
[0027] FIG. 3 is a flow diagram illustrating a method of creating
product characterizations according to the present invention.
[0028] FIG. 4 is a flow diagram illustrating a method for
generating customer characterizations according to the present
invention.
[0029] FIG. 5 is a flow diagram illustrating a method for
generating recommendations according to the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0030] The present invention can be generally characterized as
comprising two stages; a pre-processing stage followed by an
on-line querying stage. The pre-processing stage generally
comprises converting text descriptions of products available at an
e-commerce site into concise product characterizations. For
purposes of this disclosure, the term `characterization` can be
described as a set of words with appropriate weights. The weights
are defined by the frequency of occurrence of the words in the text
descriptions of the products. The characterizations can be used as
indicators of customer behavior. For example, for an application in
which movies are being recommended to a customer, the valuable
product characterizations may refer to text describing the actors
and actresses along with the class of the movie.
[0031] Once the product characterizations are created individual
customer characterizations are then created. A customer
characterization is created by concatenating each of the product
characterizations of all products which were either bought or
browsed by that customer. For purposes of the present invention,
concatenations can be described as a process of adding the sets of
words and weights to form longer characterizations. For example,
given characterization 1: heavy (1), metal (1), SONY (2); and
characterization 2: SONY (1) Music (3), the concatenation of the
two characterizations gives the following characterization:
[0032] A Concatenated Characterization
[0033] heavy (1) metal (1) SONY (3), Music (3).
[0034] The numbers in brackets correspond to the weights.
[0035] Peer groups of customers can then be formed by clustering
the customer characterizations; generally, customers exhibiting
likeminded buying/browsing habits are clustered into a peer group.
Once the clusters are generated the pre-processing stage is
complete. The pre-processing stage is performed at prespecified
timed intervals to generate the text characterizations and
clusters. Clusters are used to respond to the queries posed at the
on-line query stage.
[0036] The on-line query stage follows the pre-processing stage and
generally comprises one or more customers making queries to a
server over a network. The server in turn computes the results of
the queries and returns them to each respective customer.
[0037] Referring now to the drawings, FIG. 1 shows a preferred
architecture according to an illustrative embodiment of the present
invention. A server 8 is connected to multiple customers or clients
4 over a network 6. The clients make requests (queries) 7 to server
8 and server 8 responds to the requests by returning a result 9 for
each query made. The network 6 is an electronic commerce network
capable of facilitating product sales and purchases. The network is
preferably the Internet.
[0038] The server 8 comprises a CPU 10 for coordinating and
controlling server 8 and performs the necessary computations for
generating a query result 9. Main memory 14 acts as a data
repository for each customer 4. The customer data may also reside
on disk 15 or in cache 16. When a customer characterization is
derived from historical data, that historical data is retrieved
from the data repository for that customer.
[0039] Turning to FIG. 2, a flowchart illustrating an example of an
overall operation of the present invention, having a pre-processing
stage of steps 210 and 220, and an on-line querying stage of steps
230, 240 and 250.
[0040] At step 210, text descriptions associated with each product
at the e-commerce site are converted into product
characterizations. For purposes of the present disclosure, product
characterizations are text characterizations which have been
filtered to remove extraneous terminology (i.e. common language).
The process of converting text descriptions into product
characterizations generally includes selecting those words
contained within the text description of each product offered at
the e-commerce site which prove to be valuable indicators of
customer behavior. For example, at a site at which movies or CDS
are recommended, some examples of words with high inference power
could be "action", "romance", "mystery", "drama", etc. This is
because these words are highly indicative of customer behavior, and
are likely to have considerable skew in their distribution across
the different customers. The steps associated with creating product
characterizations will be discussed in further detail below in
connection with FIG. 3.
[0041] Next, at step 220, the product characterizations are used
for grouping customers into clusters or peer groups. Peer groups
constitute sets or clusters of customers whose purchasing/browsing
characteristics closely match that of a customer for which a
recommendation is to be made. Clustering algorithms are well known
in the art. Preferably, the clustering method described in "System
and Method for Detecting Clusters of Information," C. Aggarwal et
al., a commonly assigned patent application, Ser. No. 09/070,600,
filed on Apr. 30, 1998, is used to carry out the clustering
operation. The disclosure of that application is incorporated by
reference herein.
[0042] The on-line querying stage begins at step 230, where queries
from one or more customer are received for processing. The process
of receiving customer requests can be considered substantially
continuous in the sense that customers may issue requests to the
server at anytime or at random intervals. A customer request is
received at step 230. Based on the pre-processed data (i.e.
formation of peer groups or clusters) generated at the
pre-processing stage a response to the customer query is generated
at step 240.
[0043] At step 250, a determination is made whether a predetermined
time interval has elapsed since the pre-processing stage was last
performed. If the time interval has not elapsed, the process
remains in the 230-250 loop responding to whatever additional user
queries may exist in the queue. Otherwise, when the time interval
lapses the process returns to step 210 to perform the
pre-processing functions. This timing structure facilitates
handling of customer queries `on the fly`. Since the pre-processing
stage is more time consuming as compared to the querying stage, the
system is preferably set to perform pre-processing less frequently
but whenever the on-line querying is experiencing less traffic.
[0044] Referring next to FIG. 3, an illustrative flowchart of
creating product characterizations from text descriptions (step
210) according to one embodiment of the present invention is shown.
At step 310, the process determines the average frequency with
which each word was bought by each customer. For purposes of the
present embodiment, a word is considered bought by a customer if it
occurs in the text characterization for a product bought or browsed
by that customer. For example, if the same product was bought k
times, then the complimentary word is also counted k times and the
total frequency for that word is k. Other embodiments may consider
bought words as those texts or words browsed by a customer, or by
predefined combinations of browsing and buying. Each word bought by
a customer is counted and summed as the total frequency. At step
320, the total frequency counts are converted into a fraction, by
dividing the counts for each customer by the total frequency with
which each word was "bought" by all customers, defined as F(word,
customer). At step 330, a standard deviation of the fractions of
words which were bought by the different customers is computed. The
standard deviation for the fractions of words bought by a given
customer is computed as follows: 1 St . Dev . ( word ) = { [ 1 N (
F ( word , customer ) - 1 / N ) 2 ] / N } 1 / 2
[0045] where N is the total number of customers
[0046] In general, words from a text description having low
inference power (non-descriptive) will include generic words of the
language (i.e. is, the, an) and will expectedly have low standard
deviation values. In contrast, words which have high inference
power, which might include, for example, words such as a singer=s
name or a music category as part of a CD music product
recommendation environment will expectedly have high standard
deviation values. Those words with the highest standard deviations
are selected to create a product characterization from an input
text description of a product.
[0047] As an example, the original text description for a compact
disc could be:
[0048] Name of CD: Whitford/St. Holmes-Whitford/St. Holmes
[0049] Label: SONY MUSIC
[0050] Genre: General
[0051] Original Release Date: 1981
[0052] Engineer: George Pappas
[0053] Producer: Tom Allom
[0054] No. of Discs: 1
[0055] Mono/Stereo: Stereo
[0056] Studio/Live: Studio
[0057] Category: Heavy Metal
[0058] After performing the procedure in Flowchart 3, the
characterized words are: Heavy, Metal, General, SONY, Tom, Allom.
This is because these words are highly indicative of the nature of
the CD, while the remaining words are simply general words. Words
like Date, Label, etc. are likely to occur in each and every text
description, and therefore their distribution will not be skewed,
as all people are likely to browse these words regardless of
"taste". On the other hand, for words like "heavy", "metal" etc.,
they are likely to be highly skewed in favor of people who access
these words frequently.
[0059] Reference is now made to FIG. 4, an illustrative flowchart
describing the steps associated with step 220 of FIG. 2, that of
generating clusters (peer groups) from the generated product
characterizations. At step 410, a customer characterization is
created by concatenating the product characterizations for all
products that the customer either bought or browsed. Concatenation
in the current context means that each of the individual product
characterizations bought or browsed by a customer will be appended
to form one long characterization. At step 420, each customer is
clustered into peer groups based on the customer characterizations
formed at the previous step. The peer groups define sets of
customers who exhibit similar buying/browsing behavior. For
example: consider product characterizations created by the
following words: chip, hardware, PC, SoftwareGames, PCGames,
Joystick, processor, Aptiva, Windows98 . . . etc. In a cluster of
customers having three words `SoftwareGames, PCGames, and Joystick`
occurring repeatedly signal that this cluster of customers
correspond to a group of people who are interested in computer
games. Note that even though Joystick, PCGames and SoftwareGames
are not names of individual products, but words characterizing
products, the amount of information contained in this set of words
as belonging to a cluster is behavior information and certain
assumptions can be made about the people in that cluster.
[0060] Turning now to FIG. 5, a flowchart illustrating how the
process responds to various queries corresponding to customer
requests is provided (step 240, FIG. 2). FIG. 5 defines the on-line
query stage. It is important to note that each of the process steps
illustrated in FIG. 5 are described with regard to a single
customer request. It can be appreciated, however, that the method
can accommodate, on a first come first serve basis, a plurality of
requests from one or more customers.
[0061] At step 502, a first input is received by the system which
comprises all of the previously generated clusters or peer groups.
Next at step 505, A second and third input, e.g., a second input
buying behavior and a third input browsing behavior, are received
by the process where the second input is defined as comprising
product characterizations generated in a current on-line
buying/browsing session. The product characterizations can be
generated as specified in FIG. 3. Preferably, the generation is in
real time while a customer browses/purchases products at a current
on-line session. The third input, product characterizations are
similar to the second input, but the product characterizations are
retrieved from historical, archived data stored in main memory 14.
The generated data includes product characterizations from previous
browsing/purchasing on-line sessions specific to that customer. At
step 510, a customer characterization is generated for that
customer. Preferably, the customer characterization is constructed
utilizing the first input and clusters in conjunction with either
or both the second and third inputs. When both the second and third
inputs are utilized they are combined by weighted concatenation.
The weighted concatenation is derived by multiplying the frequency
of each word from the first and second inputs by an appropriate
weight. Taking the example given above for concatenation of the CD,
wherein characterization A: heavy (1), metal (1), SONY (2); and
characterization B: SONY (1) Music (3), if the concatenation A is
multiplied by 2, and concatenation B is multiplied by 3, the
weighted concatenation is the sum of that product, yielding: heavy
(2), metal (2), SONY (7), and Music (9). The weights correspond to
the relative importance of the buying and browsing behavior,
respectively.
[0062] At step 520, a plurality of cluster centroids are generated.
A single cluster centroid is computed for each peer group generated
at step 420 of FIG. 4. The cluster centroids are generated by
concatenating the text characterizations of all the individual
customer characterizations in that cluster (peer group). At step
530, a closest cluster centroid is found to the customer
characterization derived at step 510. A "closest" cluster centroid
to a customer characterization can be achieved by objective
functions known to one skilled in the art. Examples of objective
functions usable for a "closeness" determination include cosine
angle, the dice coefficient and the Jaccard coefficient. See
"Information Retrieval, Data Structure and Algorithms", by William
B. Frakes, and Ricardo Baeza-Yates, [Prentice Hall, 1992], which
the use of coefficients to define similarity (i.e. closeness) is
described, the disclosure therein is incorporated herein by
reference. At step 540, a list of peers are found constituting
those customers who are members of the cluster (peer group) whose
associated cluster centroid was found to be "closest" to the
customer characterization. At step 550, the peer group is utilized
to respond to the queries posed in the particular e-commerce
environment.
[0063] Examples of utilized queries will be described in
conjunction with FIG. 5. As a further note each of the following
queries requires the first input defined at step 502, however,
certain of the queries may use either one or both of the inputs
defined at step 505. The exemplary queries are;
[0064] 1. Query
[0065] For a set of products bought/browsed, find the list of all
products which form the best recommendation list.
[0066] A weighted concatenation is performed on product
characterizations of the products bought/browsed by the customer in
the current session (the second input). The third input
representing product characterizations from historical data is not
used. A report of the closest peers is made at step 540. The
products with the highest frequency bought by the closest peers are
then reported as the recommendations.
[0067] 2. Query
[0068] For a given customer, and a set of products browsed/bought
by him, find the best recommendation list.
[0069] This query is similar to query 1 except that a weighted
frequency of the third input at step 505 is now utilized along with
a weighted frequency of the second input.
[0070] 3. Query
[0071] For a given customer, find the best recommendation list.
[0072] This query is similar to the first and second, however, only
the third input from step 505 is used. In effect, the second input
is discarded.
[0073] 4. Query
[0074] Find the recommendation list out of a prespecified promotion
list for the queries (1),(2), and (3).
[0075] This query differs from the first three in that the product
recommendation list would be filtered to exclude all products
except those contained on a prespecified promotion list as
specified at step 550.
[0076] 5. Query
[0077] Find the profile of the customers who are interested in a
particular product.
[0078] This query is implemented in a way similar to that described
for query 1. It differs at step 550 of FIG. 5 in that a peer
distribution profile is output based on the product frequency for
that peer group. A peer distribution profile is a profile of the
customers in the group, such as age, sex, etc. For example, age
histograms can be output.
[0079] 6. Query
[0080] For a given profile of customers, find the products that
they would most like.
[0081] This query contemplates customer profile queries that may
take the form: For people in the 20-30 age group, what products
would they most like? This query is answered by concatenating the
text characterizations of all customers within the specified
profile set at step 510.
[0082] 7. Query
[0083] For the queries (1),(2), and (3), generate the
characterizations which would be most liked by a customer, rather
than the products themselves.
[0084] For this query the cluster centroid, derived at step 520,
provides the required product characterizations.
[0085] 8. Query
[0086] Find the closest peers for a given customer.
[0087] For this query step 530 provides the required response.
Namely, a report of the customers contained within the selected
centroid.
[0088] Advantageously, the illustrative method and system according
to the present invention facilitates characterization of customers
and products on the basis of customers natural browsing/purchasing
behavior, without resorting to group product ratings. The customer
characterizations become the means by which product recommendations
are eventually generated thereby affording substantial
personalization to the product recommendation process.
[0089] Although illustrative embodiments of the present invention
have been described herein with reference to the accompanying
drawings, it is to be understood that the invention is not limited
to those precise embodiments, and that various other changes and
modifications may be affected therein by one skilled in the art
without departing from the scope or spirit of the invention.
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