Assisted content planning: using recommendation engines to improve catalog performance

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We typically use a recommender system to estimate which content items are likely to match a user’s taste and recommend them, based on her implicit or explicit interests. In its simplest implementation, the recommender system works on a defined content catalog of items that are available to users at any given time. The catalog dataset definition is part of the process known as content planning. Content follows a fairly predetermined lifecycle: release, maintain, discontinue.
Effective content planning is important to offer a catalog of items that meet the interests of most customers. However, in several product domains, catalog “inventory” is expensive to maintain, also when it comes to digital media content. For example, in IPTV/VOD catalogs, there is a cost associated with:
  • content licensing royalties,
  • data storage costs and
  • infrastructure investments and costs for video streaming.

More importantly, it would be great to be able, given a service customer base’s preferences, to optimize which content should be in the catalog at any given time in order to maximize average revenue per user and the overall profitability of the content offering itself.

Can we use advanced algorithms to support content planning? Recommender systems may help. In fact, recommendation engines already provide data from which to infer what customers want or don’t want, such as analytics about viewing trends, performance indicators of recommendations effectiveness on items in the catalog and estimation of the users’ interests (e.g. through ratings) on potential content.

In a simple practical application of assisted content planning, for example, a digital music service feeds to the recommendation engine two catalog datasets:

  • the dataset representing the live catalog currently available to users and
  • the dataset of all licensable content (including tracks and albums not currently available)

The recommendation engine then applies collaborative, semantic and profiling techniques to identify unlicensed tracks that it WOULD recommend to a given user  if they were available to the catalog, thereby giving content planners actionable information to add potentially successful content to the live catalog.

In sum, assisted content planning allows operators to save costs by discarding items users do not find appealing while increasing their satisfaction by acquiring new content the recommender system estimates to match the users’ interests.

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