A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. A method for generating a recommendation, the method comprising: tracking, on a server including a processor, user activity associated with a user interacting with a network-based system; determining, on the server, a recommendation relationship type based at least in part on the tracked user activity, the recommendation relationship type selected from a group of recommendation relationship types including a substitute relationship type and a complement relationship type; selecting, on the server, a recommended object based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system; and generating, on the server, the recommendation for the recommended object for presentation to the user on the network-based system. 3. The method of claim 2, wherein selecting the recommended object includes evaluating a plurality of relationship scores between the first object and a plurality of second objects related to the first object according the determined recommendation relationship type. 4. The method of claim 3, wherein each relationship score of the plurality of relationship scores includes a relevance score representing an historic transactional relationship between the first object and each second object of the plurality of second objects using historical transaction data associated with the first object and each second object of the plurality of second objects. 5. The method of claim 4, wherein selecting the recommended object includes determining the maximum relevance score within the plurality of relationship scores. 6. The method of claim 4, wherein each relationship score of the plurality of relationship scores includes a navigation linkage score representing an amount of historical navigation between the first object and each second object of the plurality of second objects. 7. The method of claim 6, wherein selecting the recommended object includes determining a maximum navigation linkage score within the plurality of relationship scores. 8. The method of claim 2, wherein the determining the recommendation relationship type includes determining a purchase lifecycle location based at least in part on the tracked user activity. 9. The method claim 8, wherein determining the purchase lifecycle location includes evaluating a series of navigation operations performed by the user on the network-based system. 10. The method of claim 9, wherein determining the purchase lifecycle location includes determining from the series of navigation operations whether the user is pre-purchase or post-purchase. 11. The method of claim 10, wherein determining the recommendation relationship type includes selecting a complementary relationship type if the user is determined to be post-purchase, and selecting a substitutionary relationship type if the user is determined to be pre-purchase. 12. A computer-implemented recommendation system for use within a network-based system, the system comprising: a relationship module to calculate a relationship score for a relationship between a first object and a second object within the network-based system; a type module to select a relationship type, using the relationship score calculated by the relationship module, the relationship score representing the relationship between the first object and the second object; and a recommendation engine to provide recommendations within the network-based system using the relationship type. 13. The computer-implemented recommendation system of claim 12, wherein the type module selects the relationship type from a group of recommendation relationship types including a substitute relationship type and a complement relationship type. 14. The computer-implemented recommendation system of claim 12, wherein the relationship module is to calculate the relationship score by: calculating a relevance score representing a transactional history relationship between the first object and the second object using historical transaction data associated with the first object and the second object; accessing navigation history data from the network-based system; and calculating a navigation linkage score representing an amount of navigation between the first object and the second object using the navigation history data. 15. The computer-implemented recommendation system of claim 13, wherein the type module is to select the relationship type by: calculating a category ratio using the relevance score and the navigation linkage score; computing a complement threshold dynamically using the historical data associated with the first object and the second object; computing a substitute threshold dynamically using the historical data associated with the first object and the second object; and selecting the complement relationship type, based on the category ratio transgressing the complement threshold; and selecting the substitute relation type based on the category ratio transgressing the substitute threshold. 16. A machine-readable storage medium embodying instructions which, when executed by a computer-implemented network-based system, cause the network-based system to: track user activity associated with a user interacting with a network-based system; determine a recommendation relationship type based at least in part on the tracked user activity, the recommendation relationship type selected from a group of recommendation relationship types including a substitute relationship type and a complement relationship type; select a recommended object based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system; and generate the recommendation for the recommended object for presentation to the user on the network-based system. 17. The machine-readable storage medium of claim 16, wherein the instructions to select the recommended object include instructions to evaluate a plurality of relationship scores between the first object and a plurality of second objects related to the first object according to either a substitutionary relationship or a complement relationship. 18. The machine-readable storage medium of claim 17, wherein each relationship score of the plurality of relationship scores includes a relevance score representing an historic transactional relationship between the first object and each second object of the plurality of second objects using historical transaction data associated with the first object and each second object of the plurality of second objects. 19. The machine-readable storage medium of claim 17, wherein each relationship score of the plurality of relationship scores includes a navigation linkage score representing an amount of historical navigation between the first object and each second object of the plurality of second objects. 20. The machine-readable storage medium of claim 16, wherein the instructions to determine the recommendation relationship type further include instructions to determine a purchase lifecycle location based at least in part on the tracked user activity, and wherein the instructions to determine the purchase lifecycle location include instructions to evaluate a series of navigation operations performed by the user on the network-based system. 21. The machine-readable storage medium of claim 20, wherein the instructions to determining the purchase lifecycle location include instructions to determine from the series of navigation operations whether the user is pre-purchase or post-purchase, and wherein the instructions to determine the recommendation relationship type include instructions to select a complementary relationship type if the user is determined to be post-purchase, and select a substitutionary relationship type if the user is determined to be pre-purchase.