CODEWITHSUNDEEP
algorithmmachine-learningrecommendation-enginecollaborative-filtering
Cybermaxs
Cybermaxs

Reputation: 24558

How to manage multiple positive implicit feedbacks?

When there are no ratings, a common scenario is to use implicit feedback (items bought, pageviews, clicks, ...) to suggests recommendations. I'm using a model-based approach and I wondering how to deal with multiple identical feedback.

As an example, let's imagine that consummers buy items more than once. Should I have to consider the number of feedback (pageviews, items bought, ...) as a rating or compute a custom value ?

Upvotes: 3

Views: 1459

Answers (3)

Jacco Kuiper
Jacco Kuiper

Reputation: 51

Probably not that helpful for OP any longer, but it might be for others in the same boat.

If anyone knows more papers/methods, please share as I'm currently looking for state of the art approaches to this problem. Thanks in advance.

Upvotes: 1

greeness
greeness

Reputation: 16124

To model implicit feedback, we usually have a mapping procedure to map implicit user feedback into the explicit ratings. I guess in most domains, repeated user action against the same item indicates that the user's preference over the item is increasing. This is certainly true if the domain is music or video recommendation. In a shopping site, such a behavior might indicate the item is consumed periodically, e.g., diapers or printer ink.

One way I am aware of to model this multiple implicit feedback is to create a numeric rating mapping function. When the number of times (k) of implicit feedback increases, the mapped value of rating should increase. At k = 1, you have a minimal rating of positive feedback, for example 0.6; when k increases, it approaches 1. For sure, you don't need to map to [0,1]; you can have integer ratings, 0,1,2,3,4,5.

To give you a concrete example of the mapping, here is what they did in a music recommendation domain. For short, they used the statistic info of the items per user to define the mapping function.

We assume that the more times the user has listened to an artist the more the user likes that particular artist. Note that user’s listening habits usually present a power law distribution, meaning that a few artists have lots of plays in the users profile, while the rest of the artists have significantly less play counts. Therefore, we compute the complementary cumulative distribution of artist plays in the users’ profile. Artists located in the top 80-100% of the distribution are assigned a score of 5, while artists in the 60-80% range assign a score of 4.

Another way I have seen in the literature is to create another variable besides a binary rating variable. They call it confidence levels. See here for details.

Upvotes: 4

Sean Owen
Sean Owen

Reputation: 66886

You typically use a sum of clicks, or some weighted sum of events, as a "score" for each user-item pair in implicit feedback systems. It's not a rating, and that's more than a semantic distinction. You won't get good results if you feed these values into a process that's expecting rating-like and trying to minimize a squared-error loss.

You treat 3 clicks as adding 3 times the value of 1 click to the user-item interaction strength. Other events, like a purchase, might be weighted much more highly than a click. But in the end it also adds to a sum.

Upvotes: 0

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