Highlights of the Talk with Dr. Konstan on Recommender Systems

Highlights of the Talk with Dr. Konstan on Recommender Systems

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Highlights of the Talk with  Dr. Konstan on Recommender Systems
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Original content: Recommender Systems: (live from Farcon) at Data Skeptic

Here are some topics I've considered interesting after listening to the above Podcast on Data Skeptic.


It is the extent to which users are "positively suprised" when recommended new content.

Maximizing serendipity is an actively pursued objective of Recommender Systems, particularly in recent years.


A bit counter-intuitive - it refers to actively refraining from showing the best (most relevant) recommendations first so as to force users to keep looking at other recommendations and promote more interaction with the system.


It refers to promoting a diverse set of recommendations, so as to avoid recommending items that are too similar to one another.

Note that diversity is especially important when your customers are not repeat customers (i.e. they will very rarely come to your website, or even once only), because you may only have a single chance of conversion, so you want to hedge your bets using diverse content.

On the other hand, focusing too much on diversity may affect recurring customers, because they may want to be able to learn how you system behaves. I.e. they expect your system to behave consistently, and may not like too much variation in the recommendations.

Focus on business model

Knowing about the business model your organization wants to pursue is critical to thinking and planning good Recommender Systems.

In other words, you need to consider the precise business objective you want to achieve when designing RSs. This will guide you towards choosing what metrics you want to keep an eye on: For example:

  • Does your company want to maximize short-term item sales or long-term user retention?

  • Does your company want to maximize any sale, even if it's not the item that was recommended, or just maximize conversion for shown items?

Temporal effects

People's tastes change over time!

You need to take this into account (introduce adaptive behaviour into your models), otherwise you risk failing to capitalize on new trends and other changes in user behaviour.

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