pzn

 

  1. Personalization terms

  2. Accumulator | LikeMinds utilities
  3. Actions
  4. Allowable confidence levels
  5. Allowable rating values
  6. APIs for multivalue properties
  7. Application object
  8. Arithmetic expressions rule
  9. Example: Arithmetic operation
  10. Assigning attribute based administration rules to pages and portlets
  11. Attribute Based Administration
  12. Automatically clearing your cache entry
  13. The LikeMinds utilities
  14. Best Bets values
  15. Bindings
  16. Buildstats | LikeMinds utilities
  17. Campaigns
  18. Category beans
  19. Category beans reference
  20. CategoryEvent class
  21. Classes and APIs for writing custom listeners
  22. Clickstream Engine | LikeMinds for HCL WebSphere Portal

  23. Configuring LikeMinds utilities
  24. Configuring the Clickstream Engine
  25. Configuring the Item Affinity Engine
  26. Configuring LikeMinds
  27. Configure the LikeMinds engines
  28. Configuring the Preference engine
  29. Configuring the sifter for mentor selection
  30. Content resources
  31. Content spot exits
  32. Content spots
  33. Count of rule element

  34. Current Action Count or Name rule
  35. Current Browser Capability rule
  36. Current Date rule element
  37. Current Request Attributes rule
  38. Current Request Parameters rule
  39. Current Session Attributes rule

  40. Custom listener classes
  41. Custom log listeners
  42. Customized feedback listeners
  43. CustomLog beans
  44. CustomLog beans reference
  45. CustomLogEvent class

  46. Database performance | LikeMinds configuration
  47. Default Clickstream Engine recommendations
  48. Default Preference Engine recommendations

  49. Change content spot rule mapping
  50. Create additional advanced rules
  51. Create a content spot
  52. Create the JSP file in Rational Application Developer
  53. Create the Personalization content resource classes and content spot
  54. Creating the Personalization user resource classes and content spot
  55. Create a simple content rule
  56. Create the user profiler rule
  57. Enhance the Personalized Portlet
  58. Export the WAR file and install the portlet
  59. PZN - Coding the portlet JSP
  60. Import Personalization Workspace resource collections
  61. Insert dynamic table HTML/JSP code
  62. Personalized List portlet
  63. Modify resource collection properties
  64. Install the Personalization sample
  65. Developing a personalized portlet

  66. Prerequisites for the Personalization portlet exercise
  67. Uninstall Personalization sample and database
  68. Publishing personalization rules
  69. do Action rule element
  70. Email action or promotion
  71. Email administration
  72. Enabling or disabling the use of archetypes
  73. Enable logging
  74. Changing the error condition behavior
  75. Estimating database size | LikeMinds configuration
  76. Example: Browser capability

  77. Example: Category Count
  78. Example: "Count of"
  79. Example: Multiple profilers and optional actions
  80. Example: Nested bindings (simple)
  81. Example: Nested bindings (advanced)
  82. Example: Nested profiler
  83. Example: Request attributes and session attributes
  84. Example: Simple binding
  85. Example: Simple email action
  86. Example: Simple profiler
  87. Example: Simple select content action
  88. Example: Simple update action
  89. Example: Show page or portlet
  90. Exploring Movie Site

  91. Feedback database schema
  92. Feedback properties file
  93. Feedback schema tables
  94. Feedback subsystem overview
  95. Feedback and analytics
  96. FeedbackListener
  97. Filtering LikeMinds recommendations
  98. General tips
  99. Generic query framework
  100. Guidelines for configurable recommendation dynamics
  101. How a site is personalized
  102. Implementing action logging
  103. Implementing category logging
  104. Implementing custom logging
  105. Implementing PageView logging
  106. Implementing rating logging
  107. Include Only rule element
  108. is rule element
  109. Item Affinity Engine | LikeMinds for HCL WebSphere Portal
  110. Key value pairs

  111. LikeMinds Recommendations
  112. How LikeMinds generates recommendations
  113. LikeMinds Recommendation Engine architecture
  114. The LikeMinds Recommendation Engines
  115. LMListener
  116. Listeners and persistence
  117. LogEvent class
  118. Logging beans
  119. LogManager
  120. Rule spot mappings
  121. Maximum number of mentors assigned to each user
  122. Maximum ratings a user needs before becoming a mentor
  123. Maximum transactions a user needs before becoming a mentor
  124. Mentor selection and assignment
  125. How the mentor selection process works
  126. Mentors to look for in cache | LikeMinds engine
  127. Minimum number of Clickstream activities for a user
  128. Minimum mentors the engine examines for predictability
  129. Minimum number of ratings for user recommendations
  130. MovieSite Sample
  131. Number of mentors to use | LikeMinds engine
  132. Number of sift priority users per batch
  133. order as is rule element
  134. Portal Personalization

  135. PageView beans
  136. PageView beans reference
  137. PageViewEvent class
  138. Pausing the sifter during heavy database
  139. The Personalization interface
  140. Prediction quality values
  141. Preference Engine | LikeMinds for HCL WebSphere Portal
  142. Prepare the personalized application
  143. Preventing multiple sifters from sifting the same user
  144. Profile rule element
  145. Profiler rule element
  146. Profilers
  147. Programmatically starting rules
  148. Programming model
  149. Personalization programming reference

  150. Publishing considerations
  151. Publishing and deleting personalization rules using a script
  152. Publishing personalization rules over SSL
  153. Monitoring the status of publishing
  154. Publishing personalization rules

  155. Query framework
  156. Quick Profiler rule
  157. Ratability parameters
  158. Rating beans
  159. Rating beans reference
  160. RatingEvent class
  161. Recomputing (rebuilding) the mentor pool
  162. Recommend Content
  163. Recomputing Clickstream Engine predictions
  164. Recomputing Preference engine predictions
  165. Repeated items in visit list
  166. Reports
  167. Request Context
  168. Request Context
  169. Resource cache
  170. Resource interface
  171. ResourceInfo class
  172. Resources, resource instances, and resource collections
  173. Rule elements
  174. Rule Exception Handling in the run-time environment
  175. Rule logging
  176. RuleEvent class
  177. RuleInfo class
  178. Rules
  179. Running multiple sifters
  180. How the rules engine works
  181. Sample Personalization resources XML file
  182. Scheduling LikeMinds events
  183. sender rule element
  184. Setting the number of archetypes in cache
  185. Number of threads to sift users
  186. set to rule element
  187. Sifter | LikeMinds utilities
  188. Sifter-specific mentor set configuration
  189. Sifter performance considerations
  190. Sifter sleep time when the Lps_User_Data sift_pri field Is 0
  191. Specifying recommendation behavior
  192. Time interval for checking sift priority
  193. Use of "average user" to improve LikeMinds recommendation confidence
  194. Use and number of archetypes | LikeMinds engine

  195. User and content models
  196. User predictability
  197. User predictability | LikeMinds engine
  198. User resources
  199. Using the Personalization APIs
  200. Using the Generic Query Framework
  201. Using the LikeMinds utilities
  202. The Portal User resource collection
  203. value rule element
  204. Visibility Rules
  205. The Web Content resource collection
  206. Workload management

 

Home