Back End Server Modification And Visualization
Product recommendations are selected according to a model. Product recommendations may be annotated with rules of the model used to select the products. A user may modify the model and be provided with a URL and a cookie associated with the modified model. Subsequent requests or the URL and presenting the cookie are processed using the modified model. A context may be associated with the cookie and modified by the user to observe performance of the model for the context. An interface may permit a user to specify rules for the model or otherwise model its behavior. User interactions with a website result in recommendations according to the model. A journey is recorded that records content, recommendations, and parameters of the model corresponding to the recommendations. The journeys of users may be filtered and visually presented to a user.
1 . A system comprising a first server including one or more processing devices and one or more memory devices operably coupled to the one or more processing devices, the one or more memory devices storing executable code effective to cause the one or more processing devices to:
receive a first request including first context parameters from a user device;
process the first request and the first context parameters according to a selection model to select one or more first results from a database;
annotate the first results with rules of the model applied to select the results; and
transmit the first results and annotations to the first user device for display.
2 . The system of claim 1 , further comprising a front end server programmed to receive the first request from the first user device and forward the first request to the first server;
wherein the executable code is further effective to cause the one or more processing devices to transmit the results and annotations to the first user device by way of the front end server.
3 . The system of claim 1 , wherein the first context parameters include at least one of: attributes of a user, a user browsing history, a user search history, a user purchase history, browser parameters for a browser generating the first request on the first user device, and device parameters describing the first user device.
4 . The system of claim 1 , wherein the executable code is further effective to cause the one or more processing devices to:
receive an instruction to modify the selection model from the user device; and
in response to the instruction to modify the selection model:
copying the selection model to obtain a test model;
generate a cookie referencing the test model, the cooking having one or more second context parameters associated therewith; and
transmit the cookie to the user device.
5 . The system of claim 4 , wherein the executable code is further effective to cause the one or more processing devices to:
receive, from the user device, a second request referencing the cookie;
in response to receiving the second request referencing the cookie, select second results according to the test model and the second context parameters instead of the selection model; and
return the second results to the user device.
6 . The system of claim 5 , wherein the executable code is further effective to cause the one or more processing devices to:
receive, from the user device, an instruction to modify the second context parameters;
in response to the instruction to modify the second context parameters associated with the second cookie, select the second results according to the test model and the second context parameters as modified by the instruction to modify the second context parameters; and
return the second results to the user device.
7 . The system of claim 4 , wherein the executable code is further effective to cause the one or more processing devices to:
receive, from the user device, an instruction to pin a selected result of the second results;
in response to the instruction to pin the selected result, modify the test model to return the selected result in response to all requests.
8 . The system of claim 4 , wherein the executable code is further effective to cause the one or more processing devices to:
receive, from the user device, an instruction to pin a selected result of the second results to the second context parameters;
in response to the instruction to pin the selected result, modify the test model to return the selected result in response to all requests having the second context parameters.
9 . The system of claim 4 , further comprising:
receive, from the user device, an instruction to black list a selected result of the second results;
in response to the instruction to blacklist the selected result, modify the test model to refrain from returning the selected result.
10 . The system of claim 4 , wherein the selection model is an artificial intelligence model.
11 . A method comprising:
receiving, by a first server, a first request including first context parameters from a user device;
processing, by the first server, the first request and the first context parameters according to a selection model to select one or more first results from a database;
annotating, by the first server, the first results with rules of the model applied to select the results; and
transmitting, by the first server, the first results and annotations to the first user device for display.
12 . The method of claim 11 , further comprising
receiving, by a front end server, the first request from the first user device and forwarding the first request to the first server; and
transmitting, by the first server, the results and annotations to the first user device by way of the front end server.
13 . The method of claim 11 , wherein the first context parameters include at least one of: attributes of a user, a user browsing history, a user search history, a user purchase history, browser parameters for a browser generating the first request on the first user device, and device parameters describing the first user device.
14 . The method of claim 11 , further comprising
receiving, by the first server, an instruction to modify the selection model from the user device; and
in response to the instruction to modify the selection model:
copying, by the first server, the selection model to obtain a test model;
generate, by the first server, a cookie referencing the test model, the cooking having one or more second context parameters associated therewith; and
transmitting, by the first server, the cookie to the user device.
15 . The method of claim 14 , further comprising:
receiving, by the first server from the user device, a second request referencing the cookie;
in response to receiving the second request referencing the cookie, selecting, by the first server, second results according to the test model and the second context parameters instead of the selection model; and
returning, by the first server, the second results to the user device.
16 . The method of claim 15 , further comprising:
receive, by the first server from the user device, an instruction to modify the second context parameters;
in response to the instruction to modify the second context parameters associated with the second cookie, selecting, by the first server, the second results according to the test model and the second context parameters as modified by the instruction to modify the second context parameters; and
return the second results to the user device.
17 . The method of claim 14 , further comprising:
receiving, by the first server from the user device, an instruction to pin a selected result of the second results;
in response to the instruction to pin the selected result, modifying, by the first server, the test model to return the selected result in response to all requests.
18 . The method of claim 14 , further comprising:
receiving, by the first server from the user device, an instruction to pin a selected result of the second results to the second context parameters;
in response to the instruction to pin the selected result, modifying, by the first server, the test model to return the selected result in response to all requests having the second context parameters.
19 . The method of claim 14 , further comprising:
receiving, by the first server from the user device, an instruction to black list a selected result of the second results;
in response to the instruction to blacklist the selected result, modifying, by the first server, the test model to refrain from returning the selected result.
20 . The method of claim 14 , wherein the selection model is an artificial intelligence model.