I am trying to calculate coverage metrics for a recommender system that I have designed. This blog post talks about how to do it. I had some difficulties in understanding the same. It says that These metrics will be for user u of U users over n items out of N potential items in a recommendation list L. Each item has a content vector C of length c ... My difficulty arises in understanding N and U. Is U the total number of users? Secondly, what does N potential items mean? Is it the list of all items that are present or is it a subset of all the items present?

I am using the Python LightFM package to generate the recommendations


1 Answer 1


Right, the capital letters denote the total available. In that blog post: U means all users, N means all items but in other places is usually written I, and L means all top-n recommendation lists. "Top-n" means that the recommender system outputs a ranked list of n items, so if you had 1000 users all getting a Top-10 list, you'd have L length of 1000*10.

I suggest you read Ge, Mouzhi, Carla Delgado-Battenfeld, and Dietmar Jannach. "Beyond accuracy: evaluating recommender systems by coverage and serendipity." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010, at http://ls13-www.cs.tu-dortmund.de/homepage/publications/jannach/Conference_RECSYS10b.pdf instead. It's a more clearly written definition of metrics and uses more traditional recommender system notation.

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    $\begingroup$ The ACM paper you refer to refers to the jth recommendation time. Won't the recommendations be the same at every instance? It can only change unless there is a change in the model. So this would mean that catalog coverage would be across different models, or something of that sort. $\endgroup$ Nov 7, 2017 at 4:27
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    $\begingroup$ In that paper, their "Catalog coverage" metric is defined as the count of the union of all Top-N recommendation lists for all users, divided by the count of all items that could have been recommended. I also am a bit confused by their j and N notation. An example: you have 5 items in your database: A-E. You have 3 users who got these Top-2 lists: [A,B], [B,C], and [A,C]. The union of those lists is [A,B,C] which is 3 items. 3 divided by 5 items that could have been recommended is 3/5 or 0.6. Catalog coverage is therefore 0.6. $\endgroup$ Nov 7, 2017 at 19:35

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