Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions

Recommendations: who has not seen them? Whenever you go online, different recommendations appear for you: with similar products, with different products, based on your past purchases, or based on what other people viewed. But did you know that all these types of recommendations have different names and different effects?

Li and Karahanna (2015) review 40 empirical studies between 1990 and 2013, that focussed on the understanding of online recommendation systems (RS). An RS is basically a web-based technology, that has the ability to advise and offer a certain product that would satisfy the individual users’ needs.

Based on past literature, three stages in this so called recommendation process have been found. Stage 1 involves the understanding of the consumer (including the collection of consumer data and creating a consumer profile), as well as the delivery of recommendations to this consumer (which are match making approach and the recommendation system presentation). This is followed by a personalized recommendation (stage 2). In stage 3, the impact of the recommendation system is assessed. Stage 3 ‘flows’ back to Stage 1 in the form of feedback.

I think especially the recommendation system presentation and its effect are particularly interesting. Multiple types of RS are discussed within academic literature, such as content-based, visual, collaborative-based and social-network based recommendations. According to Li and Karahanna (2015), these types often overlap in practice, creating hybrid RS.

The content-based recommendation takes into account a consumer’s preferences, as well as his past search and purchase behaviour. A collaborative-based recommendation system does the same, but also takes into account other customers (Other customers also bought…). One main difference is that for the latter, much more data are needed, since you need data on not only one, but more customers.


An example of recommendations on Amazon 

While collaborative recommendations have as a disadvantage that new products do not have such links yet and some customers have atypical behaviour, collaborative RS are often used when it comes to alternative-based and cross-sell recommendations. The latter means that recommended items are generated across multiple, different categories, whereas the first is are mostly based on multiple customer ratings and purchases. The algorithms used for alternative-based recommendations are further based on a bunch of different customers’ clickstream data to detect preferences.

An example of a content-based recommendation is the visual recommendation. While content-based recommendations take past behaviour into account, visual recommendations do not. As expected, this type of RE shows products that are similar to another product a consumer has viewed.

So, which recommendation do you think is most effective?

That is up to you to find out (if you are still looking for a thesis topic)! While a lot of research has been done on the types of RS, limited empirical research exists on which strategies to implement to optimally use the different types of recommendation systems.

Based on some other papers and past theses I have read, I think that the visual recommendation works the least well – it will not increase the sales of one consumer, but I believe it rather shows alternatives to something they were thinking of purchasing (black dress 1, 2 or 3: that is the question). Further, while it might be nice to know what other consumers bought or viewed, I often find it irrelevant to myself. I’d rather shop-the-look if a complete outfit is shown on a model for example. However, with products other than clothes (such as books or videos) it might be different. Hence, go ahead and pick a nice thesis topic regarding these recommendations in different product categories!

Li, S., & Karahanna, E. (2015, February). Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions. Journal of Association for Information Systems, 72-107.


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