Avail RnD has been hard at work and is now ready to release a new powerful algorithm for deriving recommendations. The new algorithm works on the click data collected from users. For now it will be released on Avail SaaS and on Avail OnSite in a couple of weeks.
Sequences
To improve the quality of the recommendations sequences are taken into account. A concrete example of when using sequences is beneficial this is that if you are contemplating buying a bike, a water bottle might appeal to you. It is less likely that if you are shopping for a water bottle that you might buy a bike on the spur of the moment.
Self Improving
The new algorithm is self learning and tweaks itself based on how users respond to the recommendations thus adjusting itself to vertical specific behavior. This is all automatic and you do not have do a thing.
Selecting Data Sets
To empower advanced users a new drop-down in the Control Panel lets you choose which data set you base the recommendations on.
Avail works with three data sets:
- viewed - products clicked on during a visit
- bought-together - products bought at the same time
- bought - products bought by a user over time
To see how this could be of use, consider the recommendations based on the content of the shopping cart using the method getCartPredictions. The template has a maximum of three recommendations and two subtemplates:
- Subtemplate 1 - returns 3 recommendations, no fallback
- Subtemplate 2 - returns 3 recommendations based on the data set viewed with fallback recommendations
The effect of the above is that you will always have three recommendations. First Avail will generate as many real recommendations as possible based on the bought-together data set. If there was not enough recommendations, get recommendations from the viewed data set and if still not enough append fallback recommendations.
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