Personalization

 

  1. Accumulator
  2. Administer LikeMinds Recommendation Engines
  3. Allowable confidence levels
  4. Application object
  5. Arithmetic expressions
  6. Example: Arithmetic operation
  7. Attribute Based Administration - WebSphere Portal v6
  8. Basic LikeMinds server configuration
  9. Bindings
  10. Campaigns
  11. CategoryEvent class
  12. Clickstream Engine
  13. Personalization Concepts - WebSphere Portal v6
  14. Configure LikeMinds utilities
  15. Configure the Clickstream Engine
  16. Configure LikeMinds servers
  17. Configure the LikeMinds engines
  18. Configure the LikeMinds server for the database
  19. Configure the Preference Engine
  20. Configure the sifter for mentor selection
  21. Configure Personalization after installation
  22. Content resources
  23. Content spots
  24. Current Action Count or Action Name
  25. Current Date - WebSphere Portal v6
  26. Current Request Attributes
  27. Current Request Parameters - WebSphere Portal v6
  28. Current Session Attributes
  29. CustomLog beans
  30. CustomLog beans reference
  31. CustomLogEvent class
  32. Database performance
  33. Default Preference Engine recommendations
  34. Publishing rules
  35. Enabling or disabling the use of archetypes
  36. Enable Personalization logging - WebSpher Portal v6
  37. Example: Browser capability
  38. Example: "Count of" (quantifiable condition)
  39. Example: Nested profiler
  40. Example: Request attributes and session attributes
  41. Example: Simple binding
  42. Example: Simple e-mail action
  43. Example: Simple profiler
  44. Example: Simple select content action
  45. Example: Simple update action
  46. Example: Show page or portlet
  47. Exploring Movie Site
  48. Feedback database schema
  49. Feedback schema diagram
  50. Feedback and analytics
  51. FeedbackListener
  52. Generic query framework
  53. Guidelines for configurable recommendation dynamics
  54. Implementing category logging
  55. Implementing custom logging
  56. Implementing PageView logging
  57. Implementing rating logging
  58. Introduction to Personalization - WebSphere Portal v6.0
  59. An introduction to LikeMinds
  60. is
  61. Personalization jar files that
  62. LikeMinds Recommendation Engine
  63. The LikeMinds Recommendation Engines
  64. LMListener
  65. Logging beans
  66. LogManager
  67. Mapping symbolic database table and field names
  68. Rule spot mappings
  69. Maximum number of mentors assigned to each user
  70. Maximum ratings a user needs before becoming a mentor
  71. Maximum transactions a user needs before becoming a mentor
  72. Mentors to look for in cache
  73. Minimum number of Clickstream activities for
  74. Minimum mentors the engine examines for predictability
  75. Minimum number of ratings for user recommendations
  76. Number of mentors to use
  77. Number of sift priority users per batch
  78. Number of users and items in the cache
  79. order as is
  80. Personalization Overview
  81. PageView beans
  82. PageView beans reference
  83. PageViewEvent class
  84. Pausing the sifter during heavy database
  85. The Personalization interface
  86. Preference Engine
  87. Previewing a Recommend Content rule
  88. Profile
  89. Profiler
  90. Profilers
  91. Programmatically invoking rules
  92. Personalization programming reference
  93. Query framework - WebSphere Portal v6
  94. Quick Profiler
  95. Rating beans
  96. Recomputing Clickstream Engine predictions
  97. Recomputing Preference Engine predictions
  98. Repeated items in visit list
  99. Reports
  100. Request Context - WebSphere Portal v6
  101. Resources, resource instances, and resource collections - WebSphere Portal v6
  102. Rule elements
  103. Rule logging
  104. RuleInfo class
  105. Rules
  106. Run multiple sifters
  107. Sample Personalization resources XML file
  110. Scheduling LikeMinds Events
  111. sender
  112. Set the number of archetypes in cache
  113. Number of threads to sift users
  114. set to
  115. Sifter
  116. Sifter performance considerations
  117. Sifter sleep time when the Lps_User_Data sift_pri field Is 0
  118. Specifying cache behavior
  119. Specifying recommendation behavior
  120. Specifying transaction information on a per activity
  121. Time interval for checking sift priority
  122. Time users and items are kept in the cache
  123. Use of "average user" to improve recommendation
  124. Use and number of archetypes
  125. User and items cache management
  126. User predictability
  127. User predictability
  128. Using the Personalization APIs
  129. Using the LikeMinds utilities
  130. Use rules
  131. Visibility Rules

  132. Develop a personalized portlet

 

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