# Content Recommendations ### About this export | Field | Value | | --- | --- | | **content_type** | lesson | | **platform** | contentstack-academy | | **source_url** | https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--content-recommendations | | **course_slug** | data-insights-data-ingestion-profile-construction | | **lesson_slug** | data-insights-course-3--content-recommendations | | **markdown_file_url** | /academy/md/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--content-recommendations.md | | **generated_at** | 2026-04-28T06:55:44.160Z | > Part of **[Data Ingestion & Profile Construction](https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction)** on Contentstack Academy. **Academy MD v3** — structured for retrieval; no quiz or assessment keys. #### Video details #### At a glance - **Title:** 22-data-insights-understanding-content-recommendations - **Duration:** 4m 49s - **Media link:** https://cdn.jwplayer.com/previews/zt7iltQ2 - **Publish date (unix):** 1752880653 #### Streaming renditions - application/vnd.apple.mpegurl - audio/mp4 · AAC Audio · 113809 kbps - video/mp4 · 180p · 180p · 136938 kbps - video/mp4 · 270p · 270p · 150874 kbps - video/mp4 · 360p · 360p · 163745 kbps - video/mp4 · 406p · 406p · 172426 kbps - video/mp4 · 540p · 540p · 200843 kbps - video/mp4 · 720p · 720p · 246386 kbps - video/mp4 · 1080p · 1080p · 366924 kbps #### Timed text tracks (delivery) - **thumbnails:** `https://cdn.jwplayer.com/strips/zt7iltQ2-120.vtt` #### Transcript That's kind of how classification works at a high level. The net result is we understand the content. We have topics. Those topics are associated with the user. The thing that we haven't talked about at all is kind of then how can a customer use that information? One of the simple ways is you can build audiences, of course. I can go into here. I can build an audience. I can say that it's content, say content topics, and I want to just target anybody that has a ... We're just making up numbers on a demo site. Any number that you want to have a higher interest for this particular topic and it's going to pull up three users, so you can build a very targeted audience of just people that are going to want to engage with this particular type of content is kind of like obvious way one to use it. So Lytics out of the box for every one of our interest engines, which we'll kind of cover what ... Interest engines, what we used to call them, we're now calling them context layers as we kind of like start to rebrand some things. But for any of these interest engines, you could actually make a recommendation for a specific user from a selection of content. So when Mark visits the website, I want to surface some portion of content that we know that he's going to be interested in based on his past behaviors. If I go back over here real quick ... So if I do a recommendation, and I have not tested this in a little while, but it still works. So it's going to actually go through and it's going to select from a collection, which we'll talk about here in a second. But based on my information that I know specifically about Mark and what he is interested in, as well as the specific corpus of content that could be surfaced to Mark, here's a set of recommendations that also have scores on how well or how little they align. We have our path forward kind of web personalization tool allows you to surface content recommendations. Content recommendations is one of our most common use cases. It's the easiest thing to stand up and you can just surface a modal with recommendation for content based on whatever a user is going to want to help drive their sort of session depth essentially. So that all comes out of the box with Lytx. The APIs are already all there to support it. And then the other unique aspect of content in that sort of recommendation pipeline is what we call collections. You can think of them as just a segment of content. So again, in the same way that Lytx is building profiles, Lytx is building document profiles. If you can build an audience of Lytx users, nothing stops you from building an audience essentially of Lytx content. So recommendations are made based on content collections. So if I only want to recommend documents that have images or documents by a particular author or documents that are about dogs, whatever it is, you can build a segment of content. And then when you make a recommendation, you can choose that segment collector, that content collection to recommend from to sort of whittle down the content that you can choose from. That way you're not making just a recommendation from all of your content, you're making one only from products that want to focus on in fall or whatever it may be. So content collections, and we can walk through just creating a collection real quick, are super simple. You can go here and just say I want particular titles or authors, if it has an image, if it has a description. And then I think where I usually go is in this top right button, there's an advanced editor, which takes you into the literal segment builder, but now you're focused on a document. So you can go in here and say, you know, any, you know, content that was created after whatever certain day, things that you're going to publish in the future, certain attributes, certain brands, again, like there's some part of I think what we can unlock is that when you're building the content model, more information that you all probably already have instead of content stack could be surfaced on the document profile so that you can then segment on that information, for instance. But anyway, you can build a collection off of essentially any of the document attributes that are on there. #### Subtitles (WebVTT) ```webvtt WEBVTT 1 00:00:00.000 --> 00:00:18.600 That's kind of how classification works at a high level. 2 00:00:18.600 --> 00:00:20.520 The net result is we understand the content. 3 00:00:20.520 --> 00:00:21.520 We have topics. 4 00:00:21.520 --> 00:00:23.640 Those topics are associated with the user. 5 00:00:23.640 --> 00:00:28.280 The thing that we haven't talked about at all is kind of then how can a customer use 6 00:00:28.280 --> 00:00:29.400 that information? 7 00:00:30.400 --> 00:00:33.320 One of the simple ways is you can build audiences, of course. 8 00:00:33.320 --> 00:00:34.920 I can go into here. 9 00:00:34.920 --> 00:00:36.840 I can build an audience. 10 00:00:36.840 --> 00:00:46.960 I can say that it's content, say content topics, and I want to just target anybody that has 11 00:00:46.960 --> 00:00:54.280 a ... We're just making up numbers on a demo site. 12 00:00:54.280 --> 00:00:58.480 Any number that you want to have a higher interest for this particular topic and it's 13 00:00:58.480 --> 00:01:02.280 going to pull up three users, so you can build a very targeted audience of just people 14 00:01:02.280 --> 00:01:06.120 that are going to want to engage with this particular type of content is kind of like 15 00:01:06.120 --> 00:01:09.080 obvious way one to use it. 16 00:01:09.080 --> 00:01:13.040 So Lytics out of the box for every one of our interest engines, which we'll kind of 17 00:01:13.040 --> 00:01:17.080 cover what ... Interest engines, what we used to call them, we're now calling them context 18 00:01:17.080 --> 00:01:20.000 layers as we kind of like start to rebrand some things. 19 00:01:20.000 --> 00:01:25.600 But for any of these interest engines, you could actually make a recommendation for a 20 00:01:25.600 --> 00:01:29.160 specific user from a selection of content. 21 00:01:29.160 --> 00:01:35.440 So when Mark visits the website, I want to surface some portion of content that we know 22 00:01:35.440 --> 00:01:37.960 that he's going to be interested in based on his past behaviors. 23 00:01:37.960 --> 00:02:04.560 If I go back over here real quick ... So if I do a recommendation, and I have not tested 24 00:02:04.560 --> 00:02:06.640 this in a little while, but it still works. 25 00:02:06.640 --> 00:02:10.000 So it's going to actually go through and it's going to select from a collection, which we'll 26 00:02:10.000 --> 00:02:11.760 talk about here in a second. 27 00:02:11.760 --> 00:02:16.760 But based on my information that I know specifically about Mark and what he is interested in, as 28 00:02:16.760 --> 00:02:21.600 well as the specific corpus of content that could be surfaced to Mark, here's a set of 29 00:02:21.600 --> 00:02:26.520 recommendations that also have scores on how well or how little they align. 30 00:02:26.520 --> 00:02:31.440 We have our path forward kind of web personalization tool allows you to surface content recommendations. 31 00:02:31.440 --> 00:02:33.760 Content recommendations is one of our most common use cases. 32 00:02:33.760 --> 00:02:37.520 It's the easiest thing to stand up and you can just surface a modal with recommendation 33 00:02:37.520 --> 00:02:41.760 for content based on whatever a user is going to want to help drive their sort of session 34 00:02:41.760 --> 00:02:43.440 depth essentially. 35 00:02:43.440 --> 00:02:47.040 So that all comes out of the box with Lytx. 36 00:02:47.040 --> 00:02:49.200 The APIs are already all there to support it. 37 00:02:49.200 --> 00:02:54.640 And then the other unique aspect of content in that sort of recommendation pipeline is 38 00:02:54.640 --> 00:02:55.960 what we call collections. 39 00:02:55.960 --> 00:02:58.720 You can think of them as just a segment of content. 40 00:02:58.720 --> 00:03:04.040 So again, in the same way that Lytx is building profiles, Lytx is building document profiles. 41 00:03:04.040 --> 00:03:08.680 If you can build an audience of Lytx users, nothing stops you from building an audience 42 00:03:08.680 --> 00:03:10.940 essentially of Lytx content. 43 00:03:10.940 --> 00:03:14.840 So recommendations are made based on content collections. 44 00:03:14.840 --> 00:03:21.680 So if I only want to recommend documents that have images or documents by a particular author 45 00:03:21.680 --> 00:03:27.820 or documents that are about dogs, whatever it is, you can build a segment of content. 46 00:03:27.920 --> 00:03:31.940 And then when you make a recommendation, you can choose that segment collector, that content 47 00:03:31.940 --> 00:03:38.500 collection to recommend from to sort of whittle down the content that you can choose from. 48 00:03:38.500 --> 00:03:41.980 That way you're not making just a recommendation from all of your content, you're making one 49 00:03:41.980 --> 00:03:45.020 only from products that want to focus on in fall or whatever it may be. 50 00:03:45.020 --> 00:03:50.740 So content collections, and we can walk through just creating a collection real quick, are 51 00:03:50.740 --> 00:03:51.740 super simple. 52 00:03:51.740 --> 00:03:55.540 You can go here and just say I want particular titles or authors, if it has an image, if 53 00:03:55.540 --> 00:03:57.140 it has a description. 54 00:03:57.140 --> 00:04:00.580 And then I think where I usually go is in this top right button, there's an advanced 55 00:04:00.580 --> 00:04:05.940 editor, which takes you into the literal segment builder, but now you're focused on a document. 56 00:04:05.940 --> 00:04:13.060 So you can go in here and say, you know, any, you know, content that was created after whatever 57 00:04:13.060 --> 00:04:18.300 certain day, things that you're going to publish in the future, certain attributes, certain 58 00:04:18.300 --> 00:04:23.900 brands, again, like there's some part of I think what we can unlock is that when you're 59 00:04:23.900 --> 00:04:28.660 building the content model, more information that you all probably already have instead 60 00:04:28.660 --> 00:04:33.340 of content stack could be surfaced on the document profile so that you can then segment 61 00:04:33.340 --> 00:04:36.260 on that information, for instance. 62 00:04:36.260 --> 00:04:40.220 But anyway, you can build a collection off of essentially any of the document attributes 63 00:04:40.220 --> 00:04:40.860 that are on there. ``` ```transcript [00:00] That's kind of how classification works at a high level. [00:18] The net result is we understand the content. [00:20] We have topics. [00:21] Those topics are associated with the user. [00:23] The thing that we haven't talked about at all is kind of then how can a customer use [00:28] that information? [00:30] One of the simple ways is you can build audiences, of course. [00:33] I can go into here. [00:34] I can build an audience. [00:36] I can say that it's content, say content topics, and I want to just target anybody that has [00:46] a ... We're just making up numbers on a demo site. [00:54] Any number that you want to have a higher interest for this particular topic and it's [00:58] going to pull up three users, so you can build a very targeted audience of just people [01:02] that are going to want to engage with this particular type of content is kind of like [01:06] obvious way one to use it. [01:09] So Lytics out of the box for every one of our interest engines, which we'll kind of [01:13] cover what ... Interest engines, what we used to call them, we're now calling them context [01:17] layers as we kind of like start to rebrand some things. [01:20] But for any of these interest engines, you could actually make a recommendation for a [01:25] specific user from a selection of content. [01:29] So when Mark visits the website, I want to surface some portion of content that we know [01:35] that he's going to be interested in based on his past behaviors. [01:37] If I go back over here real quick ... So if I do a recommendation, and I have not tested [02:04] this in a little while, but it still works. [02:06] So it's going to actually go through and it's going to select from a collection, which we'll [02:10] talk about here in a second. [02:11] But based on my information that I know specifically about Mark and what he is interested in, as [02:16] well as the specific corpus of content that could be surfaced to Mark, here's a set of [02:21] recommendations that also have scores on how well or how little they align. [02:26] We have our path forward kind of web personalization tool allows you to surface content recommendations. [02:31] Content recommendations is one of our most common use cases. [02:33] It's the easiest thing to stand up and you can just surface a modal with recommendation [02:37] for content based on whatever a user is going to want to help drive their sort of session [02:41] depth essentially. [02:43] So that all comes out of the box with Lytx. [02:47] The APIs are already all there to support it. [02:49] And then the other unique aspect of content in that sort of recommendation pipeline is [02:54] what we call collections. [02:55] You can think of them as just a segment of content. [02:58] So again, in the same way that Lytx is building profiles, Lytx is building document profiles. [03:04] If you can build an audience of Lytx users, nothing stops you from building an audience [03:08] essentially of Lytx content. [03:10] So recommendations are made based on content collections. [03:14] So if I only want to recommend documents that have images or documents by a particular author [03:21] or documents that are about dogs, whatever it is, you can build a segment of content. [03:27] And then when you make a recommendation, you can choose that segment collector, that content [03:31] collection to recommend from to sort of whittle down the content that you can choose from. [03:38] That way you're not making just a recommendation from all of your content, you're making one [03:41] only from products that want to focus on in fall or whatever it may be. [03:45] So content collections, and we can walk through just creating a collection real quick, are [03:50] super simple. [03:51] You can go here and just say I want particular titles or authors, if it has an image, if [03:55] it has a description. [03:57] And then I think where I usually go is in this top right button, there's an advanced [04:00] editor, which takes you into the literal segment builder, but now you're focused on a document. [04:05] So you can go in here and say, you know, any, you know, content that was created after whatever [04:13] certain day, things that you're going to publish in the future, certain attributes, certain [04:18] brands, again, like there's some part of I think what we can unlock is that when you're [04:23] building the content model, more information that you all probably already have instead [04:28] of content stack could be surfaced on the document profile so that you can then segment ``` #### Key takeaways - Connect **Content Recommendations** back to your stack configuration before moving to the next module. - Capture one concrete artifact (screenshot, Postman call, or code snippet) that proves the step works in your environment. - Re-read the delivery versus management boundary for anything you changed in the entry model. ## Supplement for indexing ### Content summary Content Recommendations. Content Recommendations in Data Ingestion & Profile Construction (data-insights-data-ingestion-profile-construction). ### Retrieval tags - Content - Recommendations - data-insights-data-ingestion-profile-construction - lesson 14 - Content Recommendations - data-insights-data-ingestion-profile-construction lesson ### Indexing notes Index this lesson as a primary chunk tagged with lesson_id "14" and topics: [Content, Recommendations]. Parent course slug: data-insights-data-ingestion-profile-construction. Use asset_references URLs as thumbnail hints in search results when present. Never surface LMS quiz content or assessment answers from this file. ### Asset references | Label | URL | | --- | --- | | Video thumbnail: Content Recommendations | `https://cdn.jwplayer.com/v2/media/zt7iltQ2/poster.jpg?width=720` | ### External links | Label | URL | | --- | --- | | Contentstack Academy home | `https://www.contentstack.com/academy/` | | Training instance setup | `https://www.contentstack.com/academy/training-instance` | | Academy playground (GitHub) | `https://github.com/contentstack/contentstack-academy-playground` | | Contentstack documentation | `https://www.contentstack.com/docs/` |