Last week i read a really interesting scientific article from Neel Sundaresan, who works as Sr. Director & Head at the eBay Research Labs in San Jose. The article was published at the conference for Recommendation Systems in 2011 and deals with different strategies that are implemented at eBay to support customers to find relevant items inside a huge collection of available items. His article gives an excellent overview about challenges, opportunities, and approaches in building recommender systems for huge marketplaces, such as eBay. Sundaresan also states that the last decade has seen an explosive growth in research and use of recommender systems in all major e-commerce and content platforms like Netflix, eBay, Amazon, lastfm, spotify or Youtube. Beside the traditional recommendations, such as ‘People who performed action X also performed action Y‘, these engines also analyze the diversity of the users that includes collectors, value shoppers, resellers, and the complexities in shopping caused by the gap between buyer and seller languages. For e-commerce this means to think in terms of substitutes and complements and how to increase the size of a customers shopping cart. While substitutes are equivalent products which offer different prices or additional attributes, complements represent additional products that fit well with the items the customer has already within his basket.
In detail Sundaresan discusses the top-level questions of recommendation engines: 5 Ws and an H – What, When, Why, Who, Where, and How.
The What identifies and analyzes how the seller language differs from the description of an item that the customer could find attractive. The Where defines the different contexts in which a seller has the opportunity to offer items to a customer, that is in a specific mindset at this defined moment. The Why aspect addresses the transparency aspect of recommendation engines. So customers feel better, which also is reflected by the success of the approach, when there is a reason why the system proposes them an item. (thats the reason why most platforms tell you ‘People who bought this, aslo bought that’, even when the analysis in the backend is more complex than this) The Who aspect reflects who needs recommendations. A powerseller or buyer does not need the same recommendations than other customers. The When aspect defines the timeline when a customer needs, or is open for suggestions, while the How targets the technological aspects which include algorithms for designing recommendation engines. Modern approaches use content models, neighborhood models, matrix factorization models or hybrid approaches to complete the user-item matrix space.
Altogether, an excellent article, definitely worth reading!