Recommendation systems are an important part of many online services that we have grown accustomed to. They automatically suggest movies you may like to watch on Netflix, or products that you may otherwise not have noticed on Amazon. They are, however, very much still works in progress. Despite recent advances in recommendation systems, many fundamental, challenging problems still remain, such as the cold-boot problem (making good recommendations when there is a lack of sufficient data).

This paper gives a good run-down of the many different types of recommendation systems available today, highlighting their strengths and weaknesses. Considering the state of the art, the author then proposes a hybrid system that taps on the collective benefits of content-based recommendation systems and collaborative filtering systems. Key to the proposal is an integration of “individual relevance” with “social relevance.” The former captures how relevant an item is to a particular user, while the latter models the relevance of an item, taking into consideration the preferences of other like-minded users. Experiments that were run over two custom datasets showed that the proposed hybrid approach outperforms several baselines.

Thought has clearly been put into this paper, and I would recommend that anyone keen on recommendation systems should read it. The proposal is adequately motivated, and the methodology concisely described. That being said, I find that the evaluation experiments could have been more rigorous. There are publicly available datasets for recommendation systems, including the Last.fm music dataset [1], or a dataset for scholarly papers [2]. It would have been instructive to assess and benchmark the performance of the proposal with these datasets. Further, the implemented baselines are not likely to be state of the art, given that the latest system was described in a paper in 2009.

As a concluding remark, this paper describes a relatively simple hybrid recommendation system. Its relative simplicity makes it an interesting proposition because most proposals will only be as good as they are practical and feasible in real life.