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The benefits of content personalization for digital publishing
Artificial Intelligence (AI) and Machine Learning (ML) make it easy to create personalized user experiences. If you are a digital publisher, you can use AI/ML to display and recommend content that is tailored to the interests of an individual user. Personalization can improve customer satisfaction, increase user engagement, and reduce the workload of editorial and content creation staff. This blog post discusses how you can use
The benefits of personalization
The value of personalization is proven across multiple industries. A
Digital publishers achieve similar benefits from personalization. A large news site based in central Europe, for example,
Using Amazon Personalize
Publishers traditionally personalize user experiences by creating complex rules: if the user matched criteria A, B, and C, then show content X, Y, and Z. However, rule-based systems tend to be brittle and hard to maintain because they do not learn from what the user actually does. Instead, a programmer needs to manually update the rules to handle changing circumstances.
AI/ML services like Amazon Personalize offer an easier, more scalable way to make recommendations. Rather than write special code, you run an AI/ML algorithm, or recipe, on a set of data to train it to make recommendations. Amazon Personalize offers multiple recipes that handle different recommendation scenarios. To personalize content shown to users, for example, you can use the “User Recommendations” recipe.
Figure 1. Typical Interactions with Amazon Personalize
To use the recipe, you start by importing data about your users, content items, and past user interactions with your content. You then train the recipe to create a model by having it read the data, usually from an
What happens when a new content item is published, such as a breaking news story or a movie review? Since the item is new, the model doesn’t have any interactions data to know which users are interested in the content. The User Recommendations recipe solves this problem in two ways. First, the recipe automatically includes a percentage of newly published items in recommendations so that it can collect data on how users react to the content. You can control how many new items are included in recommendations when you are setting up your campaign. The second way the system recommends new items is by examining any metadata provided when you loaded your content data, such as descriptions, release dates, or keywords. The recipe uses the metadata to determine how similar the new items are to previously published content. It can then make recommendations based on this similarity. Handling new or so-called cold items is
Improving recommendations with context and metadata
The metadata you provide about your users, items, and interactions are very important for making relevant recommendations. I’ve already discussed how metadata like descriptions and keywords help Amazon Personalize recommend new content. User metadata also helps the recipe understand what interests your users. For example, if you know that readers in different regions of the country are interested in specific topics, then you should include location information in the user dataset. When Amazon Personalize makes recommendations for the user, it will take their location into consideration.
You can also add metadata to your interactions data. At a minimum, interactions must contain identifiers for your user and content item, as well as a timestamp for when the interaction occurred. Contextual metadata can also be extremely valuable. For example, users often change their reading habits depending on the device they are using. On cell phones, users may prefer shorter content items. When using a desktop or a tablet, they may prefer longer-form content. It is therefore a good idea to capture the device type in the interaction dataset so that Amazon Personalize can learn from this behavior. When you submit a recommendation request, you can include the user’s current device-type as a context parameter. Amazon Personalize will then factor that context into its response. Other contextual fields that might be important are the available bandwidth, the geographic location where the interaction is occurring, or perhaps even the weather. If you publish content about food and drink, for example, you might want Amazon Personalize to recommend more iced drinks and cool desserts when the weather is hot!
You will need to experiment with the metadata you provide in your datasets and work iteratively until you get the best recommendations for your users and content.
Should you always show users what interests them?
When personalizing content, you need to be mindful of creating “
Amazon Personalize recognizes the need for users to explore and can include so-called exploration items in its recommendations. The User Personalization recipe has exploration_weight
and exploration_item_age_cut_off parameters
that you can configure when setting up the system. If you set the weight to 0, then no exploration items are included in recommendations. If you set the weight closer to 1.0, then more items are included. The default value is 0.3. The age cutoff parameter controls the maximum age of exploration items, counted in number of days. For example, if you set the value to 7, then content over 7 days old will not be included as exploration items.
News and academic publishers are especially sensitive to how much personalized content should be displayed to users. These publishers rely on being an authoritative voice and often want to emphasize content selected by their subject matter experts. As the
The New York Times maintains editorial control by showing personalized content only in special “For You” sections on its homepage. Amazon Web Services Partner Ring Publishing takes a similar approach to balancing personalization with editorially curated content. With Ring’s solution, editors can designate a specific block on the homepage that is entirely personalized, while other portions of the homepage might be selected by the editors or contain a mix of editorially and AI-selected content.
Amazon Personalize makes it possible for you to show recommendations that are a mix of personalized and manually selected items. The User Recommendations recipe supports this use case through Promotions. In your items dataset, you first identify editorially selected content with a metadata field. For example, you might have a field called “SELECTED” that you set to “True” or “False”. When requesting recommendations, you then specify the percentage of promoted items to include, plus a promotion filter that selects the metadata field(s) that you created. In this case, the filter expression might look like this:
INCLUDE ItemID where Items.SELECTED IN (“True”)
More information on promoting content items in recommendations can be found in the
Conclusion
In this blog post, we discussed how Amazon Personalize can create better user experiences. If you are a digital publisher, personalizing content for users can:
- Increase user engagement
- Lower costs by making staff more productive
- Maximize your investment by diversifying content consumption
Personalization is not a one size-fits all solution. Every publisher needs to tailor the approach to meet business needs—how much new content versus older content should be recommended? What percentage of manually selected content should be recommended versus AI-chosen content? How much should you rely on content that aligns with user interests versus encouraging exploration of new content? What metadata and context fields are the most relevant? Publishers should treat personalization as an iterative process, which they refine and adjust over time to create the best experience for users.
As a publisher, you decide how personalization will increase customer satisfaction, lead to innovation, reduce costs, and increase your efficiency. To find out more about Amazon Personalize, visit the service