Creating recommendations using scalable technology, such as PIG, HIVE or Mahout, is one thing, but what does it take to develop an operating recommender system and what crucial customer behavioral factors are involved, what is the impact of the visual presentation of item being recommended or the way customers move from one item to another?
Some questions related to using these customer behavior factors in a multifactor recommender system are: What should be the level of personalization? How does the real time behavior of the customer constrain the recommender system? How do we measure success when everything is constantly changing?
In this talk I will address the above mentioned questions and give insight into the multifactor recommender system of online retailer bol.com. Moreover, experiences will be shared with building this recommender system and the quality of its output.