The video provides an in-depth exploration of recommendation engines, AI systems that suggest items to users by personalizing content. Leveraging machine learning algorithms, these engines identify patterns in user behavior data to create tailored suggestions. The video highlights the significant impact on business revenues, with the market for recommendation engines expected to escalate substantially in the coming years. The explanation covers the fundamental processes of these systems, including data gathering, storage, analysis, and filtering, along with a feedback loop that optimizes recommendations over time.

The video discusses various types of filtering methods used in recommendation engines, namely collaborative filtering, content-based filtering, and hybrid filtering. collaborative filtering relies on similarities between users or items, whereas content-based filtering focuses on the attributes of the items themselves. hybrid filtering combines these two approaches to potentially enhance recommendation accuracy. These methods are exemplified by well-known companies like Netflix, which use such technologies to improve user experiences.

Main takeaways from the video:

Please remember to turn on the CC button to view the subtitles.

Key Vocabularies and Common Phrases:

1. recommendation engine [rek.ə.mənˈdeɪ.ʃən ˈɛn.dʒɪn] - (noun) - An AI system that suggests items to a user by personalizing content. - Synonyms: (suggestion system, referral system)

A recommendation engine is an AI system that suggests items to a user, essentially personalizes content, and that's a big deal.

2. explicit data [ɪkˈsplɪs.ɪt ˈdeɪ.tə] - (noun) - Data involving user actions and activities, such as comments or ratings. - Synonyms: (stated data, clear data, overt data)

Now, one of those is called explicit data.

3. implicit data [ɪmˈplɪs.ɪt ˈdeɪ.tə] - (noun) - User behavior data such as clicks, past purchases, and search history. - Synonyms: (implied data, indirect data, inferred data)

Now, the other type of data, that is called implicit data.

4. collaborative filtering [kəˈlæb.əˌrætɪv ˈfɪltərɪŋ] - (noun) - A filtering system that suggests items based on users' likeness to others. - Synonyms: (cooperative filtering, joint filtering, collective filtering)

A collaborative filtering system filters suggestions based on a particular user's likeness to others.

5. content-based filtering [ˈkɒn.tent-beɪst ˈfɪltərɪŋ] - (noun) - A filtering system focused on item features to make recommendations. - Synonyms: (attribute-based filtering, feature-based filtering)

content-based filtering, which filters recommendations based on an item's features.

6. hybrid filtering [ˈhaɪ.brɪd ˈfɪltərɪŋ] - (noun) - A combination of collaborative and content-based filtering methods. - Synonyms: (mixed filtering, integrated filtering)

hybrid filtering, which as you probably guessed, combines both collaborative filtering and content-based filtering.

7. demographics [ˌdɛ.məˈɡræf.ɪks] - (noun) - Statistical data relating to the population and particular groups within it. - Synonyms: (population statistics, population data)

demographics like age and psychographics, like interests and lifestyles.

8. psychographics [ˌsaɪ.kəˈɡræf.ɪks] - (noun) - The study of personality, values, opinions, attitudes, interests, and lifestyles. - Synonyms: (psychological factors, lifestyle analytics)

demographics like age and psychographics, like interests and lifestyles.

9. matrix factorization [ˈmeɪ.trɪks ˌfæk.tə.raɪˈzeɪ.ʃən] - (noun) - A method for breaking down a matrix into simpler, more manageable components. - Synonyms: (matrix decomposition, matrix simplification)

Model based, and it uses algorithms to predict user preferences by identifying patterns in user behavior. And one common method is matrix factorization.

10. data lakehouse [ˌdeɪ.tə ˈleɪk.haʊs] - (noun) - A system that stores both structured and unstructured data, combining features of data lakes and warehouses. - Synonyms: (data hub, integrated data storage)

Or it might be a data lake house, which kind of combines the best of both worlds.

What is an AI Recommendation Engine?

I often start these videos by asking you what is. What is some technical AI term or other. But I think we're all familiar with recommendation engines. They suggest which video to watch next, which songs you might like, which products you might be interested in, all based on using machine learning algorithms to find patterns in user behavior, data to create suggestions, personalized just for you. But do you understand how they work? Well, let's get into it.

A recommendation engine is an AI system that suggests items to a user, essentially personalizes content, and that's a big deal. So according to research by McKinsey, personalization can raise revenues something like between 5 and 15%. Now, the recommendation engine market, that's estimated to be valued today at something like $6.88 billion and is expected in the next five years to triple. So with that in mind, let's get into how recommendation engines work, the types of recommendation engines, and the why, as in why use them in terms of benefits and challenges?

And let's start here with the how so to target users with suitable suggestions. A recommendation engine typically operates in five different phases. The first of those is called data gathering. The data gathering phase. Now, the more we know about a given user, the more fuel we'll have to guide the other four phases. And there are two types of data that recommendation engines make use of.

Now, one of those is called explicit data. Now, explicit data covers user actions and activities, like comments a user has posted, online, reviews the user has written, and content the user has rated in some way. Hmm. Ratings. You know what, that reminds me. Now would be a great time to click the thumbs up button on this video because both I and the YouTube recommendation engine would greatly appreciate it. Now, the other type of data, that is called implicit data, and that's user behavior, like clicks, past purchases, and search history.

Now, you might be thinking, I never post online reviews. I do all my web searching in incognito mode. So recommendation engines, they won't have any data on me. Well, maybe so, but there are other people out there that share similar characteristics as you. demographics like age and psychographics, like interests and lifestyles. And recommendation engines can use this data to personalize the content for you.

Now, after the data has been gathered, the next step is that we need to store it somewhere so storage comes next. Now, that might be in a data warehouse, which can aggregate data from different sources. It might be a data lake, which can store both structured and unstructured data. Or it might be a data lake house, which kind of combines the best of both worlds with the data Stored. We can now move on to phase three, and that is analysis.

So this is all about using machine learning algorithms to process and examine data sets. These algorithms detect patterns, identify correlations, and weigh the strength of those patterns and correlations. Once they've done that, we move into a pretty important stage, which is the filtering stage. Now, filtering stages is filtering the data showing the most relevant items from the previous analysis phase.

And we'll get more into filtering in just a moment. But also, you know, like any good machine learning algorithm, there is a fifth stage as well, and that is the feedback loop that we put on the end here. And the feedback loop regularly assesses the outputs of the recommendation system, observes if and how the user action those recommendations, and then uses that data to optimize the model, hopefully enhancing its accuracy and quality over time.

Okay, so let's narrow in now on filtering. Recommendation engines differ based on the filtering method that they use. And there are generally three types. So let's take a look at them. And the first type is called collaborative filtering. So let's take a look at collaborative filtering.

Now, a collaborative filtering system filters suggestions based on a particular user's likeness to others. Now, these systems assume that users with comparable preferences will likely be interested in the same items and potentially interact with them in similar ways in the future. Actually, there are two main types of collaborative filtering systems, and one of those is memory based.

Now, memory based represents users and items as a matrix. They extend the KNN algorithm, that's the KNEE algorithm, where they aim to find their nearest neighbors in the matrix, which can be similar users or similar items. Now, memory based filtering can also be split down into two things. So we've got item based and we've got user based.

In the item based filtering, the system focuses on how users interact with the items to find similarities between the items themselves. So for example, if a bunch of users rate or interact with two items in a similar way, those items are considered similar. Now, on the other hand, user based, that compares users based on their behavior and preferences, recommending items that similar users have liked.

Now, that's memory. The other type of collaborative filtering that is called model based, and it uses algorithms to predict user preferences by identifying patterns in user behavior. And one common method is matrix factorization, where a large user item matrix is simplified to kind of squash down into a smaller set of factors.

All right, so that's collaborative filtering. The second type of filtering method that is called content based. So content based filtering, which filters recommendations based on an item's features. So this really is all about focusing in on features. So unlike collaborative filtering, which relies on user behavior, content based filtering looks at the specific attributes of the items themselves, things like keywords or product descriptions, and recommends items with similar features to those a user has interacted with before.

And this approach works pretty well when detailed information about the item is available. And it's especially useful for new or niche items that haven't really been widely rated or reviewed by users yet. Okay, now the third type of filtering that's simply called hybrid hybrid filtering, which as you probably guessed, combines both collaborative filtering and content based filtering, potentially overcoming some of the limitations of each of those methods.

And a well known example of hybrid filtering is Netflix's recommendation engine, which combines collaborative filtering based on user ratings with content based filtering, using information like genre or actors to suggest movies or shows.

All right, let's wrap this up by looking at the why why do this? What are the benefits and challenges a recommendation engine can bring to both businesses and users?

Right. So in the benefits column, I think we need to include improved user experience as a potential benefit here. Recommending the right product or the service that the user wants saves the user time from scrolling endlessly through an extensive catalog. And in fact, something like 80% of what viewers watched on Netflix comes from suggestions powered by recommendation algorithms.

Now, it can also lead to higher customer retention as well. According to research firm McKinsey, this enhanced customer experience, it can translate to something like 20% higher customer satisfaction. And if it's done well, well, ultimately it can lead to higher revenue as well. In fact, 35% of what shoppers buy on Amazon comes from product recommendations.

But those are the benefits. There are challenges as well. Let's talk about some of those. And one of those is there is an increase in cost and there's an increase in complexity. All of that analyzing and filtering massive amounts of data, well, it requires complex architectures and see significant investment in computing resources.

Another concern is what if we get bad recommendations? Yeah, that's always a concern. It's always a risk. If algorithms are optimized around the wrong metrics, items that are often highly rated might be suggested more frequently than new or obscure ones, but it might not be what the customer is actually interested in.

And we must also be concerned about bias creeping in here as well. Machine learning algorithms might learn societal biases present in data, or they might learn it from human evaluators who tune the model, resulting in inaccurate recommendations. So that's recommendation engines. You'll find them everywhere. E commerce, media and entertainment, travel and hospitality. An AI recommendation engine. It's only as good as the data it's built on and the filtering method applied. But when implemented correctly, it can really transform the user experience.

And if a recommendation engine happened to lead you to this video, well, I'd say it's working like a charm.

Technology, Business, Science, Recommendation Engines, Machine Learning, User Experience, Ibm Technology