Collaborative filtering is a method of calculating the similarities among
a group of customers and using the buying and navigation activities of the group members as the basis for making product recommendation and prediction for a current user closly resembling the group. The feature has been implemented using the "LikeMinds" software.
LikeMinds is a collaborative filtering engine that analyzes user interactions that occur on your Web site and generates real time predictions and recommendations to your Web site users, which are displayed using Web activities.
LikeMinds cannot be installed in the Developer version.
Understanding Collaborative Filtering
As a simple example, if a particular customer looks at a book, the collaborative filtering application determines the group of customers that have either looked at, or purchased the same book. The application then generates a list of possible recommendations from the other items that the members of the established group have also looked at or purchased. The relevance of any particular recommendation is determined by the number of group members that have looked at the item being recommended. That is, if 75% of the group have purchased the item being considered for recommendation, then it would be highly relevant, compared to an item that only one member of the group has looked at. Finally, the most relevant recommendations are displayed to the current customer.
Multiple data sources may be used for making recommendations. Few examples are -
LikeMinds is a collaborative filtering engine that analyzes user interactions that occur on your Web site and generates real time predictions and recommendations to your Web site users, which are displayed using Web activities.
LikeMinds cannot be installed in the Developer version.
Understanding Collaborative Filtering
As a simple example, if a particular customer looks at a book, the collaborative filtering application determines the group of customers that have either looked at, or purchased the same book. The application then generates a list of possible recommendations from the other items that the members of the established group have also looked at or purchased. The relevance of any particular recommendation is determined by the number of group members that have looked at the item being recommended. That is, if 75% of the group have purchased the item being considered for recommendation, then it would be highly relevant, compared to an item that only one member of the group has looked at. Finally, the most relevant recommendations are displayed to the current customer.
Multiple data sources may be used for making recommendations. Few examples are -
- Clickstream events that capture the details of a customer's session
- Data from existing company databases
When a user visits your Web site, WebSphere Commerce captures clickstream
data which is stored in your database. For example, the following
types of interactions may be recorded:
- Products a user has purchased
- Items added or removed from a shopping basket
- A history of the user's navigation throughout the application
Clickstream Recommendation Engine
The Clickstream Engine tracks clickstream (or rating) behavior and generates recommendations based on mentors who exhibit similar content and product affinities. The Clickstream Engine tracks the pages that users have looked at. As this data is collected, it is analyzed to identify users' traffic patterns. Finally, the Clickstream engine, then makes content recommendations for each specific user, using data from relevant subsets of the user base.
Inappropriate usage examples can be Credit Card Vendor(the recommendation are better when based on business rules - higher balance limits , lower annual percentage rates etc. Also , there are lower transaction and return visit rates) and Wedding dress retailer (lower transaction per session and return visit rates )
How to enable this feature?
This feature is not available for the Developer version.
More details on how this feature can be enabled can be found in the Infocenter at the below link
Collaborative Filtering
The Clickstream Engine tracks clickstream (or rating) behavior and generates recommendations based on mentors who exhibit similar content and product affinities. The Clickstream Engine tracks the pages that users have looked at. As this data is collected, it is analyzed to identify users' traffic patterns. Finally, the Clickstream engine, then makes content recommendations for each specific user, using data from relevant subsets of the user base.
Typically, after a user has completed a minimum number of transaction
activities, that user is assigned a set of mentors. A mentor is a
specially designated user who has visited the Web site a number of
times, and whose profile is similar to the user's. LikeMinds uses
a technique called collaborative filtering to build a mentor's profile
for each user to predict how much a user will like particular items
and which items that user will enjoy, buy, or add to their shopping
cart.
Where this feature can be used?
The Collaborative Filtering is recommended for retailers who satify the following -
- There are very large variety and number of items to recommend.
- The choices and promotions are typically based upon strong personal opinions rather than rules.
- Have sufficiently hight interaction rates and return visit rates.
Inappropriate usage examples can be Credit Card Vendor(the recommendation are better when based on business rules - higher balance limits , lower annual percentage rates etc. Also , there are lower transaction and return visit rates) and Wedding dress retailer (lower transaction per session and return visit rates )
How to enable this feature?
This feature is not available for the Developer version.
More details on how this feature can be enabled can be found in the Infocenter at the below link
Collaborative Filtering
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