Title goes here

[due_date_calculator widget_title="Roast calculator" date_picker_title="Type of meat" menstrual_cycle_picker_title="Weight" button_title="Calculate" /]

In many ways, personalization is a good thing. Internet users are so inundated with content, that filtering through the noise and presenting only information of interest can reduce user effort and enrich online experiences.

Many users know that every interaction online is tracked and analyzed. All of this data is tagged and segmented to create individualized customer profiles, which drive the delivery of personalized content (stories, products, ads, and information) to us online. But when does personalization become a problem?

In our research, we observed some of the downsides of personalization on the web. In particular, one of the problems with a personalized experience is that users are placed into a niche and start experiencing only information that goes into that niche. However, individuals are often multifaceted and change over time. A system that caters to a single user facet risks becoming boring or even annoying and can miss opportunities.

Highlight box content

It goes here!

Precision and Recall in Personalization

The problem with overpersonalization is similar to balancing precision and recall in information retrieval (IR).
In information retrieval:

• Precision is the percentage of retrieved search results that are relevant.
• Recall is the percentage of all relevant results that the search system actually retrieves.

When applied to search, these metrics define the relevance of search results shown to users in relation to all possible relevant results in order to assess and optimize the search tool. For example, one of Google’s original competitive advantages was that it recognized that precision was more important than recall in the context of searching an almost infinite data source like the web — in other words, displaying only relevant results for a query is better than returning every result that could potentially be relevant (at the penalty of including many low-value hits).

Similarly, many digital products appear to be prioritizing precision over recall in personalization as well — trying to make sure that recommended content is definitely relevant to the user. As a consequence, they adhere strictly to what they already know about users (topics of interest, liked content, etc.). However, the exact same strategy that’s great for searching is less useful for browsing and monitoring.

For example, let’s imagine a user of an app for browsing images and video. This user has two primary interests — she loves both cats and dogs.

In this imaginary app, there are 100 cat posts and 100 dog posts that could be shown to the user (plus thousands of posts about other topics). She would love to see any of the 200 cat or dog posts, but she’s only liked or shared cat posts so far. This platform doesn't have any indication that she likes dogs. As a result, it shows her only the cat posts.

The user is getting a very precise, relevant set of posts, but she’s missing out on the potential diversity of content topics. Ideally, she’d like to see both cat posts and dog posts, but the app isn’t optimizing to its fullest potential and allowing her to discover new and interesting content. Furthermore, maybe this user would think that a platypus was the cutest thing ever if she ever saw one, but the handful of platypus photos in the app will never be shown.

Overpersonalization is dangerous both for organizations and users: (1) users see the same type of content again and again, with little chance of expanding their horizons and interests; (2) companies get fewer opportunities to learn about their users.

This approach treats users like one-dimensional characters, rather than the complex multifaceted individuals that they are.

This is a quote