Summary: Cross-Domain Recommendations with Overlapping Items

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D. Kotkov, S. Wang, and J. Veijalainen. Cross-domain recommendations with overlapping items. In Proceedings of the 12th International Conference on Web Information Systems and Technologies, pages 131-138, 2016.

Cross-Domain Recommendations with Overlapping Items

If you are not familiar with recommender systems, you might want to read definitions provided here.

Suppose you would like to run your own music recommendation service. You have audio recordings and a few users. You can suggest recordings to users based on attributes of recordings. For example, if a user likes Madonna, then you can suggest more songs of Madonna. However, this way to suggest recordings (Content-based filtering) has quite low performance. If you had user ratings, you would use collaborative filtering, but you do not have them in the beginning. What you could do is to collect ratings from another service like Last.fm. In this paper, we wanted to find out whether ratings from another service would help improve recommendation performance.

It might seem obvious that more data should help us recommend better, but that might not be the case. For example, the service you want to collect ratings from might have users with completely different behavior from the behavior on your service.

To answer this question, we collected the dataset from Russian online social network vk.com. We then matched this dataset with ratings collected from Last.fm (not all the recordings were matched). The results are rather straightforward. More data improves the results. We also discovered that the more items have ratings from both systems (overlapping), the better improvement. Low number of overlapping items creates a bias in the data, which might decrease the recommendation performance.

In a nutshell

• The combination of sources improves the recommendation accuracy.
• The improvement increases with the growth of items that have ratings from both systems.