Architectural considerations on search relevancy in the context of e-commerce

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With an increasing amount of relevancy factors, relevancy fine-tuning becomes more complex as changing the impact of factors produces increasingly more unintended side effects. In recent years, there has been a lot of discussion about how learning algorithms can replace manual relevancy fine-tuning in order to manage this complexity. However, discussions about the challenge of relevancy should additionally consider architectural aspects. Especially microservice-based architectures provide many ways to encapsulate and to separate complexities of search solutions, which facilitates optimizing the search as well as locating and fixing problems.

Generally, relevancy factors can be assigned to three different groups, each handled at a different stage of the search request processing. The first group contains contextual factors that depend on certain characteristics of a query, such as query-related boosts lifting up top-sellers for queries or category-related boosts to distinguish products from their accessories. Such contextual factors can be handled as a step of the preprocessing of queries. The respective boosting information can simply be appended to the query before it is actually sent to the search engine. Ideally, the normalization of the query is done beforehand.

The second group contains factors that are considered for all queries in more or less the same way, e. g. a ranking function basing on keyword occurrences, product topicality or sales in total. Factors related to this group can be handled directly by configuring the search engine.

The third group contains situational factors. For instance, a certain product might be a good match for a certain query in general, but for situational circumstances it should not appear among the top five products (e. g. because it is out of stock). Such situational factors can be handled by resorting result sets, after they were returned by the search engine.

The handling of the different factors within successive stages of search request processing will be discussed from an architectural perspective. Implications for applying learning algorithms and the implementation of a personalized search will be considered.