com.recombee.api_client.api_requests.RecommendItemsToItem Maven / Gradle / Ivy
package com.recombee.api_client.api_requests;
/*
This file is auto-generated, do not edit
*/
import java.util.Date;
import java.util.Map;
import java.util.HashMap;
import com.recombee.api_client.util.HTTPMethod;
/**
* Recommends set of items that are somehow related to one given item, *X*. Typical scenario is when user *A* is viewing *X*. Then you may display items to the user that he might be also interested in. Recommend items to item request gives you Top-N such items, optionally taking the target user *A* into account.
* It is also possible to use POST HTTP method (for example in case of very long ReQL filter) - query parameters then become body parameters.
*/
public class RecommendItemsToItem extends Request {
/**
* ID of the item for which the recommendations are to be generated.
*/
protected String itemId;
/**
* ID of the user who will see the recommendations.
* Specifying the *targetUserId* is beneficial because:
* * It makes the recommendations personalized
* * Allows the calculation of Actions and Conversions
* in the graphical user interface,
* as Recombee can pair the user who got recommendations
* and who afterwards viewed/purchased an item.
* If you insist on not specifying the user, pass `null`
* (`None`, `nil`, `NULL` etc. depending on language) to *targetUserId*.
* Do not create some special dummy user for getting recommendations,
* as it could mislead the recommendation models,
* and result in wrong recommendations.
* For anonymous/unregistered users it is possible to use for example their session ID.
*/
protected String targetUserId;
/**
* Number of items to be recommended (N for the top-N recommendation).
*/
protected Long count;
/**
* If *targetUserId* parameter is present, the recommendations are biased towards the user given. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
*/
protected Double userImpact;
/**
* Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
*/
protected String filter;
/**
* Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
*/
protected String booster;
/**
* If item of given *itemId* or user of given *targetUserId* doesn't exist in the database, it creates the missing entity/entities and returns some (non-personalized) recommendations. This allows for example rotations in the following recommendations for the user of given *targetUserId*, as the user will be already known to the system.
*/
protected Boolean cascadeCreate;
/**
* Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
*/
protected String scenario;
/**
* With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user.
* Example response:
* ```
* {
* "recommId": "0c6189e7-dc1a-429a-b613-192696309361",
* "recomms":
* [
* {
* "id": "tv-178",
* "values": {
* "description": "4K TV with 3D feature",
* "categories": ["Electronics", "Televisions"],
* "price": 342,
* "url": "myshop.com/tv-178"
* }
* },
* {
* "id": "mixer-42",
* "values": {
* "description": "Stainless Steel Mixer",
* "categories": ["Home & Kitchen"],
* "price": 39,
* "url": "myshop.com/mixer-42"
* }
* }
* ]
* }
* ```
*/
protected Boolean returnProperties;
/**
* Allows to specify, which properties should be returned when `returnProperties=true` is set. The properties are given as a comma-separated list.
* Example response for `includedProperties=description,price`:
* ```
* {
* "recommId": "6842c725-a79f-4537-a02c-f34d668a3f80",
* "recomms":
* [
* {
* "id": "tv-178",
* "values": {
* "description": "4K TV with 3D feature",
* "price": 342
* }
* },
* {
* "id": "mixer-42",
* "values": {
* "description": "Stainless Steel Mixer",
* "price": 39
* }
* }
* ]
* }
* ```
*/
protected String[] includedProperties;
/**
* **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
*/
protected Double diversity;
/**
* **Expert option** If the *targetUserId* is provided: Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested relevancy, and may return less than *count* items when there is not enough data to fulfill it.
*/
protected String minRelevance;
/**
* **Expert option** If the *targetUserId* is provided: If your users browse the system in real-time, it may easily happen that you wish to offer them recommendations multiple times. Here comes the question: how much should the recommendations change? Should they remain the same, or should they rotate? Recombee API allows you to control this per-request in backward fashion. You may penalize an item for being recommended in the near past. For the specific user, `rotationRate=1` means maximal rotation, `rotationRate=0` means absolutely no rotation. You may also use, for example `rotationRate=0.2` for only slight rotation of recommended items.
*/
protected Double rotationRate;
/**
* **Expert option** If the *targetUserId* is provided: Taking *rotationRate* into account, specifies how long time it takes to an item to recover from the penalization. For example, `rotationTime=7200.0` means that items recommended less than 2 hours ago are penalized.
*/
protected Double rotationTime;
/**
* Dictionary of custom options.
*/
protected Map expertSettings;
/**
* Construct the request
* @param itemId ID of the item for which the recommendations are to be generated.
* @param targetUserId ID of the user who will see the recommendations.
* Specifying the *targetUserId* is beneficial because:
* * It makes the recommendations personalized
* * Allows the calculation of Actions and Conversions
* in the graphical user interface,
* as Recombee can pair the user who got recommendations
* and who afterwards viewed/purchased an item.
* If you insist on not specifying the user, pass `null`
* (`None`, `nil`, `NULL` etc. depending on language) to *targetUserId*.
* Do not create some special dummy user for getting recommendations,
* as it could mislead the recommendation models,
* and result in wrong recommendations.
* For anonymous/unregistered users it is possible to use for example their session ID.
* @param count Number of items to be recommended (N for the top-N recommendation).
*/
public RecommendItemsToItem (String itemId,String targetUserId,long count) {
this.itemId = itemId;
this.targetUserId = targetUserId;
this.count = count;
this.timeout = 3000;
}
/**
* @param userImpact If *targetUserId* parameter is present, the recommendations are biased towards the user given. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
*/
public RecommendItemsToItem setUserImpact(double userImpact) {
this.userImpact = userImpact;
return this;
}
/**
* @param filter Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
*/
public RecommendItemsToItem setFilter(String filter) {
this.filter = filter;
return this;
}
/**
* @param booster Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
*/
public RecommendItemsToItem setBooster(String booster) {
this.booster = booster;
return this;
}
/**
* @param cascadeCreate If item of given *itemId* or user of given *targetUserId* doesn't exist in the database, it creates the missing entity/entities and returns some (non-personalized) recommendations. This allows for example rotations in the following recommendations for the user of given *targetUserId*, as the user will be already known to the system.
*/
public RecommendItemsToItem setCascadeCreate(boolean cascadeCreate) {
this.cascadeCreate = cascadeCreate;
return this;
}
/**
* @param scenario Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
*/
public RecommendItemsToItem setScenario(String scenario) {
this.scenario = scenario;
return this;
}
/**
* @param returnProperties With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user.
* Example response:
* ```
* {
* "recommId": "0c6189e7-dc1a-429a-b613-192696309361",
* "recomms":
* [
* {
* "id": "tv-178",
* "values": {
* "description": "4K TV with 3D feature",
* "categories": ["Electronics", "Televisions"],
* "price": 342,
* "url": "myshop.com/tv-178"
* }
* },
* {
* "id": "mixer-42",
* "values": {
* "description": "Stainless Steel Mixer",
* "categories": ["Home & Kitchen"],
* "price": 39,
* "url": "myshop.com/mixer-42"
* }
* }
* ]
* }
* ```
*/
public RecommendItemsToItem setReturnProperties(boolean returnProperties) {
this.returnProperties = returnProperties;
return this;
}
/**
* @param includedProperties Allows to specify, which properties should be returned when `returnProperties=true` is set. The properties are given as a comma-separated list.
* Example response for `includedProperties=description,price`:
* ```
* {
* "recommId": "6842c725-a79f-4537-a02c-f34d668a3f80",
* "recomms":
* [
* {
* "id": "tv-178",
* "values": {
* "description": "4K TV with 3D feature",
* "price": 342
* }
* },
* {
* "id": "mixer-42",
* "values": {
* "description": "Stainless Steel Mixer",
* "price": 39
* }
* }
* ]
* }
* ```
*/
public RecommendItemsToItem setIncludedProperties(String[] includedProperties) {
this.includedProperties = includedProperties;
return this;
}
/**
* @param diversity **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
*/
public RecommendItemsToItem setDiversity(double diversity) {
this.diversity = diversity;
return this;
}
/**
* @param minRelevance **Expert option** If the *targetUserId* is provided: Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested relevancy, and may return less than *count* items when there is not enough data to fulfill it.
*/
public RecommendItemsToItem setMinRelevance(String minRelevance) {
this.minRelevance = minRelevance;
return this;
}
/**
* @param rotationRate **Expert option** If the *targetUserId* is provided: If your users browse the system in real-time, it may easily happen that you wish to offer them recommendations multiple times. Here comes the question: how much should the recommendations change? Should they remain the same, or should they rotate? Recombee API allows you to control this per-request in backward fashion. You may penalize an item for being recommended in the near past. For the specific user, `rotationRate=1` means maximal rotation, `rotationRate=0` means absolutely no rotation. You may also use, for example `rotationRate=0.2` for only slight rotation of recommended items.
*/
public RecommendItemsToItem setRotationRate(double rotationRate) {
this.rotationRate = rotationRate;
return this;
}
/**
* @param rotationTime **Expert option** If the *targetUserId* is provided: Taking *rotationRate* into account, specifies how long time it takes to an item to recover from the penalization. For example, `rotationTime=7200.0` means that items recommended less than 2 hours ago are penalized.
*/
public RecommendItemsToItem setRotationTime(double rotationTime) {
this.rotationTime = rotationTime;
return this;
}
/**
* @param expertSettings Dictionary of custom options.
*/
public RecommendItemsToItem setExpertSettings(Map expertSettings) {
this.expertSettings = expertSettings;
return this;
}
public String getItemId() {
return this.itemId;
}
public String getTargetUserId() {
return this.targetUserId;
}
public long getCount() {
return this.count;
}
public double getUserImpact() {
return this.userImpact;
}
public String getFilter() {
return this.filter;
}
public String getBooster() {
return this.booster;
}
public boolean getCascadeCreate() {
if (this.cascadeCreate==null) return false;
return this.cascadeCreate;
}
public String getScenario() {
return this.scenario;
}
public boolean getReturnProperties() {
if (this.returnProperties==null) return false;
return this.returnProperties;
}
public String[] getIncludedProperties() {
return this.includedProperties;
}
public double getDiversity() {
return this.diversity;
}
public String getMinRelevance() {
return this.minRelevance;
}
public double getRotationRate() {
return this.rotationRate;
}
public double getRotationTime() {
return this.rotationTime;
}
public Map getExpertSettings() {
return this.expertSettings;
}
/**
* @return Used HTTP method
*/
@Override
public HTTPMethod getHTTPMethod() {
return HTTPMethod.POST;
}
/**
* @return URI to the endpoint including path parameters
*/
@Override
public String getPath() {
return String.format("/recomms/items/%s/items/", this.itemId);
}
/**
* Get query parameters
* @return Values of query parameters (name of parameter: value of the parameter)
*/
@Override
public Map getQueryParameters() {
HashMap params = new HashMap();
return params;
}
/**
* Get body parameters
* @return Values of body parameters (name of parameter: value of the parameter)
*/
@Override
public Map getBodyParameters() {
HashMap params = new HashMap();
params.put("targetUserId", this.targetUserId);
params.put("count", this.count);
if (this.userImpact!=null) {
params.put("userImpact", this.userImpact);
}
if (this.filter!=null) {
params.put("filter", this.filter);
}
if (this.booster!=null) {
params.put("booster", this.booster);
}
if (this.cascadeCreate!=null) {
params.put("cascadeCreate", this.cascadeCreate);
}
if (this.scenario!=null) {
params.put("scenario", this.scenario);
}
if (this.returnProperties!=null) {
params.put("returnProperties", this.returnProperties);
}
if (this.includedProperties!=null) {
params.put("includedProperties", this.includedProperties);
}
if (this.diversity!=null) {
params.put("diversity", this.diversity);
}
if (this.minRelevance!=null) {
params.put("minRelevance", this.minRelevance);
}
if (this.rotationRate!=null) {
params.put("rotationRate", this.rotationRate);
}
if (this.rotationTime!=null) {
params.put("rotationTime", this.rotationTime);
}
if (this.expertSettings!=null) {
params.put("expertSettings", this.expertSettings);
}
return params;
}
}
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