Search is an integral component of most websites, whether it be a content site or a commerce site. The search personalization capabilities available in COTS (Commercial Off The Shelf) and generally implemented are still rudimentary rule based behavior and cater to a large set of users and therefore lacks hyper personalization. From this pattern you will be able to gauge the user’s context & intent and deliver an optimized, personalized search result and reduce the number of clicks for a user to get to the content or product.

This pattern demonstrates a methodology to personalize search results, by identifying clear-cut affinities/preferences across various categories that the customer has ordered for in the past.

When the reader has completed this pattern, they will understand how to develop search personalization and boost search results, in accordance with each customer’s preferences, using the IBM WebSphere Commerce and IBM PCI (Predictive Customer Intelligence).

The intended audience for this pattern are practitioners or business users who want to demonstrate intent and context driven personalization to their end customers, architects and senior developers who want to understand and learn technologies on how to develop and built personalization scenarios, such as in this case- context driven search.

1. User initiates search in WCS storefront
2. User profile data is exported from WCS to a file repository
3. Order data is exported from WCS to a file repository
4. User profile data is imported into PCI from the file repository, for analysis.
5. Order data is imported into PCI from the file repository, for analysis.
6. The PCI models establish affinity for each user across various categories based on the order data from WebSphere Commerce. The User data enriched with scores and affinity attributes is churned out from PCI.
7. This user-affinity data from PCI is fed into WebSphere Commerce and used to enrich the existing search – the affinity data is used to filter the search results.

WebSphere Commerce: IBM WebSphere Commerce is an omni-channel commerce platform that gives you the customer and business insight needed to engage shoppers as individuals with personalized content and offers, deliver mobile optimized experiences and more quickly respond to market opportunities to grow your business.

Predictive Customer Intelligence: PCI is an analytical tool that helps with reaching customer objectives and improving customer experience by analyzing the available data and predicting next best actions. The analytics in PCI is driven by IBM SPSS Modeler.

1. Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.
2. J2EE: A secure Object-oriented programming language, used to build applications. IBM WebSphere Commerce platform is built on J2EE framework which includes (JSP, Java, Java Script, Struts, REST & Webservices, EJBs)

• Install WebSphere Commerce Developer Version 8
• Publish ExtendedSites.sar by logging to WebSphere Commerce Admin Console. This publishes the Aurora B2C storefront.
• Set up PCI

Follow these steps to setup and run this developer pattern. The steps are described in detail below.

Execute the following query in DB to populate ATTR.FIELD1 that’s used to identify preferred/affinity attributes:

UPDATE ATTR SET FIELD1=1;
UPDATE ATTR SET FIELD1=0 WHERE ATTR_ID IN (SELECT ATTR_ID FROM ATTRDESC WHERE LANGUAGE_ID=-1 AND NAME IN ('Available Sizes'));
UPDATE ATTR SET FIELD1=0 WHERE ATTR_ID IN (SELECT ATTR_ID FROM ATTRDESC WHERE LANGUAGE_ID=-1 AND NAME IN (Size'));
UPDATE ATTR SET FIELD1=0 WHERE ATTR_ID IN (SELECT ATTR_ID FROM ATTRDESC WHERE LANGUAGE_ID=-1 AND NAME IN (Brand'));
UPDATE ATTR SET FIELD1=0 WHERE ATTR_ID IN (SELECT ATTR_ID FROM ATTRDESC WHERE LANGUAGE_ID=-1 AND NAME IN ('Price'));

Use the data extraction utility in WCS to extract order and user data.

• Extract the user profile details by using the data extract utility in WebSphere Commerce. This is invoked by triggering the following command from WC_HOME/bin directory: dataextract.cmd

• Extract the order details by triggering the following command from WC_HOME/bin directory: dataextract.cmd wc-dataload.xml uses the configuration files - wc-extract-order.xml and wc-extract-users.xml

Select the starting node in PCI stream, and browse for the order and user files, exported from WCS.

• The files are processed by the SPSS Modeler to associate with user affinity/preference data. The input files would be order & user files in csv format extracted from WCS.

• The output files with the user-enriched data would be placed in the output path specified.

• Clone This Repo

• Navigate to spss_stream

• Open the SPSS stream with SPSS modeler, double click on Orders.csv node browse and select the input file, click ok & do a right click and hit run. The output file for user affinity will be saved at the specified location.

• Open the SPSS stream with SPSS modeler, double click on Pageview_new.csv node browse and select the input file, click ok & do a right click and hit run. The output file for user synonyms will be saved at the specified location.

• The screenshot of the SPSS stream is below which has the rules for transformation of data and the stream is available at the spss_stream folder.

Enable Facet for your selected attribute like eq: Material below

CMC >> Catalog Tool >> Select Aurora Store >> Select Attribute Dictionary Attribute in the search box besides Material (as shown in Screen 1). Make attribute Facetable and searchable as shown in the screen below.

Screen 1:

Screen 2:

• Changes done to the file wc-search.xml: searchProfile IBM_findProductsBySearchTerm changes to include a new provider, ExtSearchBoostExpressionProvider

• GetUserAffinityDataBean: This data bean fetches the user attribute affinity and attribute preference.

• ExtSearchDisplayCmdImpl: This is a new command which extends OOTB SearchDisplayCmd and performs search.

• GetSynonymDictionaryBySearchTermDB: This data bean fetches the user synonym dictionary based on the supplied search term.

• SearchResultsDisplay.jsp: The changes done in this file includes the customization to display a personalized message for custom search “Your search result page has been personalized. Please click here for non personalized search”. Clicking on the hyperlink for non-personalized search invokes the nonPersonalizeSearch() javascript function from Search.js.

Also, a new dojo function is added to keep functionality working (fetching personalized and non-personalized results) even on page refresh.

• Search_UI.jspf: The form searchBox in the jspf is changed and new param userPrefSearch is passed to ExtSearchDisplayCmd. This change is required to add in the two new checkboxes for Self and Gift, within the Search box.

• Search.js: Two new javascript methods nonPersonalizeSearch() and userPreferredSearch() are added for out-of-box search and customized search. The latter is invoked on selecting the checkboxes introduced for Self and Gift search.

• SearchSetup.jspf: This file is modified to invoke GetUserAffinityDataBean and preferred attributes are passed to the search cluster.

CREATE TABLE USER_CAT_AFFINITY ( 
	USERS_ID BIGINT NOT NULL,    
    CATID BIGINT NOT NULL,
	BRAND VARCHAR (254),
	LASTUPDATE TIMESTAMP,	
	CONSTRAINT USER_CAT_PRICE_AFFINITY_PK PRIMARY KEY (USERS_ID, CATID), 
	CONSTRAINT USER_CAT_PRICE_AFFINITY_FK FOREIGN KEY (USERS_ID) REFERENCES USERS (USERS_ID) ON DELETE CASCADE	
);
CREATE TABLE USER_ATTR_AFFINITY ( 
	UATTR_ID BIGINT NOT NULL,
	USERS_ID BIGINT NOT NULL, 
	CATID BIGINT NOT NULL,
	ATTRFACETNAME VARCHAR (254),    
    ATTRFACETVALUE VARCHAR (254),
	ISATTRPREF INTEGER NOT NULL,
	LASTUPDATE TIMESTAMP,
	CONSTRAINT USER_ATTR_AFFINITY_PK PRIMARY KEY (UATTR_ID),
	CONSTRAINT USER_ATTR_AFFINITY_UK UNIQUE (USERS_ID, CATID, ATTRFACETNAME, ATTRFACETVALUE, ISATTRPREF), 
	CONSTRAINT USER_ATTR_AFFINITY_FK FOREIGN KEY (USERS_ID) REFERENCES USERS (USERS_ID) ON DELETE CASCADE,
	CONSTRAINT USER_ATTR_AFFINITY_UCAT_FK FOREIGN KEY (USERS_ID, CATID) REFERENCES USER_CAT_AFFINITY (USERS_ID, CATID) ON DELETE CASCADE
);

Make entry in KEYS/SUBKEYS tables

INSERT INTO KEYS VALUES((select  min(KEYS_ID)-1  from keys),'user_attr_affinity','UATTR_ID',10000,500,0,9223372036849999872,3,'0',1048576);

Execute below SQL statement by updating CMDREG

INSERT INTO CMDREG (STOREENT_ID, INTERFACENAME, CLASSNAME, TARGET) VALUES (0, 'com.ibm.commerce.catalog.commands.SearchDisplayCmd', 'com.ext.commerce.catalog.commands.ExtSearchDisplayCmdImpl', 'Local');

Once the code and Database changes are effected, ensure that the WebSphere Commerce Server is restarted.

Login into WCS as one of the existing users, who had placed prior orders across multiple categories. This data would have been analyzed by PCI and the user would have a strong affinity established.

Search for a category and notice that the search results are tuned in accordance with the brand/size affinity established by PCI.

See DEBUGGING.md.

This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer [Certificate of Origin, Version 1.1 (DCO)] (https://developercertificate.org/) and the [Apache Software License, Version 2] (http://www.apache.org/licenses/LICENSE-2.0.txt).

ASL FAQ link: http://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN