[SEARCH] [CONTEXT] Customize your search results

In software development and computer systems there has always been a conflict between the little “humanity” of the engines and the interactions with it.

Social analysis brings us an undeniable statement: we are all different.

Although fashion displays a cyclic model in which seasons and styles are standardized for most people, we must always consider that the user has his/her personal tastes and that this should be reflected in their interaction with computer systems. The same should happen with search engines and their results, no search is the same as any person is, so we must try to make people feel unique and special when performing their searches so they have a feeling that the search engine “understands” them.

To achieve this, at empathy.co we have developed Context! It ’s a customisation system that will allow us to generate an user profile, so that the searches fit the client’s tastes and preferences. You can find more about Context functionalities here.

HOW TO APPLY CONTEXTUALISATION TO YOUR SEARCHES

The first step is to _activate the contextualisation for your searches. O_ur integrations experts will cset up the most interesting fields for each use case and, from that moment, interactions and searches of each user will generate a totally anonymous preference profile that will allow personalising the results.

So … tell me what you want, what you really, really want.

In the following visual example we can see how Mel C and Vicky achieve different results in the search for jeans thanks to that customisation.

SO … HOW DO I ACCESS CONTEXTUALISATION DATA?

These data, also called affinity model, can be obtained thanks to the anonymous user identifier that is generated when entering e-commerce and is divided into two parts; user preferences, related to the features of the products seen and purchased during each search, and a list of the products visited by the user. In the following script we can access this data with a small and simple script made in GO:

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packagemainimport("fmt""net/http""net/url""encoding/json")const(//HeadercontentType="Content-Type"applicationJson="application/json"//URLsuserContextUrl="https://api.empathybroker.com/contextsearch/v2/usercontext/all/{your_site}"userAffModelUrl="https://api.empathybroker.com/contextsearch/v2/usercontext/preferences/{your_site}"userLastProdUrl="https://api.empathybroker.com/contextsearch/v2/usercontext/products/{your_site}"//Request constantsuserParam="user"queryParam="query"rowsParam="rows")varhttpClient=&http.Client{}//Handle errors in http request and print status and headersfunchandleHttpResponse(resp*http.Response,errerror){//Handle erroriferr!=nil{panic(err)}//Print detailsfmt.Println("Http Response Status: ",resp.Status)fmt.Println("Http Response Headers: ",resp.Header)//Print responsevarresultmap[string]interface{}json.NewDecoder(resp.Body).Decode(&result)fmt.Println("Response: ",result)}//Method to get all the user affinity model preferencesfuncgetUserAffinityModel(userstring,rowsstring){baseUrl,err:=url.Parse(userAffModelUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(userParam,user)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}//Method to get the user last products viewedfuncgetUserLastProductsViewed(userstring,querystring,rowsstring){baseUrl,err:=url.Parse(userAffModelUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(userParam,user)params.Add(queryParam,query)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}//Method to get all the user personalisation modelfuncgetUserContext(userstring,querystring,rowsstring){baseUrl,err:=url.Parse(userContextUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(userParam,user)params.Add(queryParam,query)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}funcmain(){//Get user affinity modelgetUserAffinityModel("userId","2")//Get user last products viewedgetUserLastProductsViewed("userId","query","2")//Get complete user contextgetUserContext("userId","query","2")}

Our customisation system also includes a system that allows you to customise without the user personal information, since we will analyze the general data of the searches and their interactions for ALL users. In this way we will generate a general affinity model as well as a list of the most viewed products for each search, so that we can obtain these preferences for each search and offer the user much more adjusted results to the trends. In the following script we can access this data with a small and simple script made in GO:

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packagemainimport("fmt""net/http""net/url""encoding/json")const(//HeadercontentType="Content-Type"applicationJson="application/json"//URLsqueryContextUrl="https://api.empathybroker.com/contextsearch/v2/querycontext/all/{your_site}"queryAffModelUrl="https://api.empathybroker.com/contextsearch/v2/querycontext/attributes/{your_site}"queryLastProdUrl="https://api.empathybroker.com/contextsearch/v2/querycontext/products/{your_site}"//Request constantsqueryParam="query"rowsParam="rows")varhttpClient=&http.Client{}//Handle errors in http request and print status and headersfunchandleHttpResponse(resp*http.Response,errerror){//Handle erroriferr!=nil{panic(err)}//Print detailsfmt.Println("Http Response Status: ",resp.Status)fmt.Println("Http Response Headers: ",resp.Header)//Print responsevarresultmap[string]interface{}json.NewDecoder(resp.Body).Decode(&result)fmt.Println("Response: ",result)}//Method to get all the query affinity model preferencesfuncgetQueryAffinityModel(querystring,rowsstring){baseUrl,err:=url.Parse(queryAffModelUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(queryParam,query)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}//Method to get the query top products viewedfuncgetQueryLastProductsViewed(querystring,rowsstring){baseUrl,err:=url.Parse(queryLastProdUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(queryParam,query)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}//Method to get all the query personalisation modelfuncgetQueryContext(querystring,rowsstring){baseUrl,err:=url.Parse(queryContextUrl)iferr!=nil{fmt.Println("Malformed URL: ",err.Error())return}//Complete query paramsparams:=url.Values{}params.Add(queryParam,query)params.Add(rowsParam,rows)// Add query params to the URLbaseUrl.RawQuery=params.Encode()//Do http requestreq,_:=http.NewRequest(http.MethodGet,baseUrl.String(),nil)req.Header.Set(contentType,applicationJson)resp,err:=httpClient.Do(req)handleHttpResponse(resp,err)}funcmain(){//Get query affinity modelgetQueryAffinityModel("query","2")//Get query last products viewedgetQueryLastProductsViewed("query","2")//Get complete query contextgetQueryContext("query","2")}

Sounds good right? This service can be consumed and integrated into your own search system, but it is also fully integrated into ours, so you can start using contextualisation now!