Understanding the limitations of recommendation systems

Recommendation engines use predictive algorithms to suggest recommendations to a bunch of users. It is a powerful technology, but we should be aware of its limitations. Let’s look into the various limitations of recommendation systems.

The cold start problem

At the core of collaborative filtering lies a crucial dependency: historical user data. Without a track record of user preferences, generating accurate suggestions becomes a challenge. For a new entrant into the system, the absence of data means our algorithms largely operate on assumptive grounds, which can lead to imprecise recommendations. Similarly, in content-based recommendation systems, fresh items might lack comprehensive details, making the suggestion process less reliable. This data dependency – the need for established user and item data to produce sound recommendations – is what’s termed the cold start problem.

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