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      Signals Types and Structures

      Signals types and structure

      Signals can be broadly categorized as implicit or explicit. When signals are enabled, Fusion produces several built-in signal types by default, all of which are implicit signals. You can also create custom signal types, including explicit signals. Be sure to verify that your signals include all of the important fields for best results. It is also useful to rank your signal types in terms of how strongly each type indicates a user’s interest in an item.

      Implicit signals vs explicit signals

      Signals can reveal a user’s level of interest in an item in two main ways:

      • Implicit

        The user shows interest by engaging with the item/document through clicks, searches, and so on. Since this type of interaction requires no additional effort on the user’s part, these types of signals tend to be plentiful. They can be used to infer a measurable value of interest in order to build an accurate recommender system.

      • Explicit

        An explicit signal is created when a user intentionally assigns a clear, measurable value to an item, such as by giving it a rating. This value can be used to rank items, for example. Since this requires the user to invest extra time to provide the information, the number of ratings tends to be small compared to the total number of users interacting with the item.

      You can create recommendations based on implicit signals out of the box. For recommenders based on explicit signals, contact your Lucidworks Professional Services representative.

      Built-in signal types

      There are five built-in signal types:

      Annotation signals

      Annotation signals are generated when a user bookmarks, likes, or comments on a document. Annotation signals are likewise generated when the user removes a bookmark, like, or comment.

      Annotation signals are generated by App Studio. If you are not using App Studio, this type of signal is not relevant to your search application.

      Login signals

      Login signals record information about specific users when they log in to an application. This includes a time stamp and various session details.

      Request signals

      A request signal is generated by a front-end search app and captures the raw user query and other contextual information about a user and their journey through the search app. A request signal should have the following fields:

      [
        {
          "id":"288fe4f7-6680-403e-8d18-27647cdd9989",
          "timestamp":1518717749409,
          "type":"request",
          "params":{
            "user_id":"admin",
            "session":"ef4e00cd-91bb-45b4-be80-e81f9f9c5b27",
            "query":"USER QUERY HERE",
            "app_id":"SEARCH APP ID",
            "ip_address":"0:0:0:0:0:0:0:1",
            "host":"Lucids-MacBook-Pro-5.local",
            "filter":[
              "field1/value",
              ...
            ],
            "filter_field":[
              "field1"
            ]
          }
        }
      ]

      Additional optional fields are used by App Insights. In the raw signal, optional fields should be inside the params object. Optional fields are as follows:

      "page_title":"Fusion Search",
      "path":"/search",
      "browser_type":"Browser",
      "browser_version":"64.0.3282.140",
      "browser_name":"Chrome",
      "referrer":"http://localhost:8080/",
      "ctx_prev_uri":"/",
      "ctx_prev_query":"",
      "ctx_prev_path":"/",
      "os_manufacturer":"Apple Inc.",
      "os_name":"Mac OS X",
      "os_id":"778",
      "os_device":"Computer",
      "os_group":"Mac OS X"

      Response signals

      Response signals are automatically generated by a query pipeline when the signals feature is enabled for a collection.

      Front-end search applications should not send response signals to Fusion directly, as those would conflict with the auto-generated signals.

      A response signal has the following explicit fields, plus any additional query parameters sent by the search application for a query:

      Field Name Description Example

      id

      The x-fusion-query-id generated by the query-pipeline used for associating click signals with queries in experiments and aggregation jobs.

      TwWCn3Dz

      type

      Signal type

      response

      response_type

      Used by Insights to determine if this query had results or was empty

      results | empty

      session

      User session ID; the search app should pass the session ID in the query params for a query

      UUID

      query

      The actual query string sent to Solr from Fusion

      ipad

      query_orig_s

      The incoming query from the search app before it is enriched by the query pipeline

      ipad

      query_id

      A hash generated from the session, query, and filters fields; used as a rollup key in Insights to group activity by a specific

      SHA1 hash

      filters_s

      Filter queries sent to Solr; the Fusion SearchLogger component combines multiple fq parameters into a single value delimited by " $ "

      {!tag=format}format:(vhs) $ {!tag=type}type:(movie)

      filter

      Reformatted filter queries for use by App Insights

      field1/value

      user_id

      User ID; the search app should pass the user_id in the query params

      admin

      doc_ids_s

      A comma-delimited list of document IDs returned for the page of results; this field is used by Fusion Spark jobs, such as the ground truth job, to perform click/skip analysis

      123,456,789

      pipeline_id

      Fusion query pipeline that processed this query

      _system

      collection

      Fusion collection

      my_collection

      qtime

      Query time from Solr, in milliseconds

      10

      rows

      Number of rows requested for this query

      10

      hits

      Total number of documents matching the query

      10000

      totaltime

      Total processing time of this query in milliseconds, includes Solr qtime and Fusion query processing time

      15

      timestamp_tdt

      Timestamp when the query request was received by Fusion

      2018-02-15T18:17:42.560Z

      res_offset

      Offset of results; this field is used by experiment metrics to calculate MRR

      0

      res_pos

      Position of the clicked result within the list of results

      3

      params.*

      Any other query param sent from the search app to Fusion that was not already mapped to a declared field

      params.defType_s=edismax

      Fusion’s experiment framework relies heavily on response signals and the linking between response and clicks signals using the fusion_query_id.

      Click signals

      Click signals are sent from the search app to Fusion. All click signals should include a fusion_query_id field pulled from the query response header x-fusion-query-id. In addition, click signals should include the following fields:

      [
        {
          "id":"SOME UUID HERE",
          "timestamp":1518725351750,
          "type":"click",
          "params":{
            "fusion_query_id":"ABkaEA11",
            "user_id":"admin",
            "session":"b3a15101-9e30-4e28-8a23-d1f663c2ee06",
            "query":"tiger woods",
            "ctype":"result",
            "res_offset":0,
            "filter":[
              "type/Game"
            ],
            "ip_address":"0:0:0:0:0:0:0:1",
            "host":"Lucids-MacBook-Pro-5.local",
            "doc_id":"9502308",
            "app_id":"SEARCH APP ID",
            "res_pos":1,
            "filter_field":[
              "type"
            ]
          }
        }
      ]

      Additional optional fields are used by App Insights. In the raw signal, optional fields should be inside the params object. Optional fields are as follows:

      "browser_type":"Browser",
      "browser_version":"64.0.3282.140",
      "browser_name":"Chrome",
      "referrer":"http://localhost:8080/",
      "ctx_prev_uri":"/",
      "ctx_prev_query":"",
      "ctx_prev_path":"/",
      "os_manufacturer":"Apple Inc.",
      "os_name":"Mac OS X",
      "os_id":"778",
      "os_device":"Computer",
      "os_group":"Mac OS X"
      "url":"http://localhost:8080/#/product/9502308",
      "label":"Tiger Woods PGA Tour 09 All-Play - Nintendo Wii",

      Custom signal types

      The signal type parameter can also take arbitrary values for custom signal types. For example, you can create special signals for purchase events, cart addition/subtraction events, "favorite" or "like" events, customer service events, and so on.

      To collect custom signals, configure your front-end search application to send signals to Fusion using a custom value for the type field. Custom signals should also include the fields described below in order to get the best results from aggregation and recommendation jobs.

      To use custom signals in recommendations, you must add them to the value of the signalTypeWeights parameter in the configuration for the _user_item_preferences_aggregation job and the _user_query_history_aggregation job.

      Custom signals can be analyzed in App Insights just like pre-defined signal types.

      Important fields for signals

      The jobs that aggregate signals and generate recommendations work best when all of the following fields are present in your signals:

      Field Name Example Value Description

      count_i

      1

      Number of times an interaction event occurred with this item

      doc_id

      NMDDV

      Product ID or Item ID

      id

      68f66808-6bfc-4d73-95f7-8a558529160b

      The signal ID. If no ID is supplied, one will be automatically generated.

      query

      xwearabletech

      A query string from the user

      session_id

      91aa66d11af44b6c90ccef44d055cf9a

      Id for session in which user generated the signal

      type

      quick_view_click

      Type of session the user used to interact with the platform

      user_id

      11506893

      ID of user during the session

      timestamp_tdt

      2018-11-20T17:58:57.650Z

      Time when signal was generated

      Some signal types, including custom signal types, may include additional fields.

      Parameter suffixes

      Fusion can add suffixes when fields are indexed. This table lists common suffix values.

      Single Value Suffix Multivalued Sufix Type

      *_b

      *_bs

      boolean

      *_d

      *_ds

      double

      *_dt

      *_dts

      date

      *_f

      *_fs

      float

      *_i

      *_ii

      int

      *_l

      *_ls

      long

      *_s

      *_ss

      string

      *_t

      *_ts

      text

      Signal field count analysis

      Lucidworks recommends performing signal field count analysis to determine whether any of the fields above are missing from some of your signals.

      The table below shows how to query for specific fields using the Query Workbench in order to compare the number of results for each field with the total number of documents in the signals collection. In the examples in the third column, some fields appear in all 33,477,919 signals documents, while others appear in fewer documents.

      Field name Query Example number of documents

      ALL

      *:*

      33,477,919

      count_i

      count_i:[* TO *]

      11,101,165

      doc_id

      doc_id:[* TO *]

      23,216,297

      id

      id:[* TO *]

      33,477,919

      query

      query:[* TO *]

      19,724,598

      session_id

      session_id:[* TO *]

      11,101,165

      type

      type:[* TO *]

      33,477,919

      user_id

      user_id:[* TO *]

      26,117,399

      timestamp_tdt

      timestamp_tdt:[* TO *]

      26,117,399

      You can also get the number of signals documents that contain all of the required fields by using the following query:

      count_i:[* TO *] doc_id:[* TO *] id:[* TO *] query:[* TO *] type:[* TO *] user_id:[* TO *] timestamp_tdt:[* TO *] session_id:[* TO *]

      The query_id field

      For each incoming signal, Fusion calculates a value for the query_id field, which App Insights uses to create group-by-query reports like the one shown below:

      Facet filters applied report

      The query_id field should not be confused with the fusion_query_id, which is a unique ID for each query processed by a Fusion query pipeline, or with query_s which is the query string.

      To calculate the value, Fusion creates a hash based on session, query, and filter fields, then saves it into the query_id field.

      The filter field can either be passed in by the search app, or computed by the SignalFormatterStage (the first stage in the _signals_ingest pipeline) using the raw filter queries. For instance, on a response signal that is generated by a query pipeline, the following fq query params get translated into the multi-valued filter field:

      • Raw query parameters:

        fq={!tag=format}format:(VHS)&fq={!tag=type}type:(Movie)
      • filters_s field (created by the SearchLogger component):

        {!tag=format}format:(vhs) $ {!tag=type}type:(movie)
      • filter field:

        "filter":["format/VHS", "type/Movie"]

      App Insights uses the filter field to generate various reports.

      Signal type ranking

      When you have defined some custom fields, it is useful to rank them according to how strongly they indicate a user’s interest in an item. While it is not necessary to exclude certain signal types from the main signals collection, some can be excluded from signal aggregations in order to focus on the most important fields when generating recommendations.