Analytics: Wie Deep Learning das Internet personalisiert

  • Beitrags-Kategorie:Analytics

Deep Learning ist einer der größeren Hype-Begriffe bei der Datenanalyse. Heute mal ein interessanter Bericht, wie Deep Learning das Internet personalisiert. Spannend!

We are pretty sure that deep learning is going to be the next big leapfrog ahead in the field of personalization as well. Personalization constitutes more and more an area of focus for businesses ranging from eCommerce stores to publishers and marketing agencies due to its proven potential to drive sales, increase engagement and improve overall user experience. If data is the fuel of personalization, than recommender systems are its engine. The advances in these algorithms have a profound effect on the online experiences of users across domains and platforms.

Here we look at three specific areas where deep learning can complement and improve existing recommender systems.
Incorporating the content into the recommendation process

Item-to-item recommendations represent a standard task for recommender systems. This means, when an eCommerce store or publisher site recommends another product or piece of content that is similar to the one currently being viewed by the user. One typical approach to tackling this task is based on metadata (another typical data source is user interactions that fuel the Amazon-like “users who bought this item also bought…” logics). However, the poor quality of metadata is a recurring problem in a large percentage of real life situations: values are missing or are not assigned systematically. Even if meta-tags are perfect, such data only represents the actual item much more indirectly and in less detail than a picture of it, for instance. With the help of deep learning, the actual, intrinsic properties of the content (images, video, text) could be incorporated into recommendations. Using DL, item-to-item relations could be based on a much more comprehensive picture of the product and would be less reliant on manual tagging and extensive interactional histories.

Tackling the cold-start problem

The cold-start is the arch-enemy of recommendation systems. It can affect both users and items. For users, the cold-start means when the system has limited or no information on the customer’s behavior and preferences. The item cold-start represents the lack of user interactions with the data upon which item-to-item relations can be drawn (we still have the metadata, though, but that won’t often suffice for truly fine-tuned recommendations). The item cold-start is an obvious domain for the aforementioned content-based approach as it makes the system less reliant on transactional and interactional data.

However, creating meaningful personalized experiences for new users is a much trickier problem that cannot necessarily be solved by simply gathering more information on them. It is quite typical – especially in the case of eCommerce sites or online marketplaces with wide product portfolios – that customers visit a website with completely different goals over time. First they come to buy a microwave, but the next time they’re looking for a mobile phone. In this scenario, the data gathered in their first session is not relevant to the second.

The Four Moments of Truth

The four moments of truth are the brief time periods when customers make their decisions based on the company’s communication and the available information provided by them. These decisions are heavily influenced by long-term, personal preferences, and brand loyalty, but momentary impressions are also major factors. A deep learning-fueled approach to influencing customers during these Moments of Truth could lead to further, novel insights about the intrinsic human decision process.

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