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’s 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.

Note
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.

Note
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_id

NMDDV

Product ID or Item ID

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. Descriptions for click signal field can be found in the table above for response signals.

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

Note
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’s 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.