While ‘Recommender Systems’ is an established research discipline, the questions of how to generate good recommendations, what good recommendations are, and how to measure ‘goodness’ are far from being answered.
To get an idea of ‘hot’ and promising research topics, have a look at the recent workshops of the ACM Recommender Systems conference, and other related conferences.
Our personal list of highly interesting research topics include
Surprisingly, it is relatively unknown how much impact recommender systems actually have on a business’s success, its customers, and society. Early research reports that recommender systems increase sales and site activity by up to 30%. But as Sharma et al. (2015) point out, “these estimates likely overstate the true causal estimate, possibly by a large amount”. Consequently, there is still lots of work to do to identify the true impact, and discussing what ‘impact’ actually means in the context of recommender systems. Eventually, recommender systems span far beyond simply maximizing revenue but also should consider the consequences e.g. for society (think of filter bubbles, etc).
Automated Recommender Systems (AutoRecSys)
MULPURU, S. 2006. What you need to know about Third-Party Recommendation Engines. Forrester Research.
GRAU, J. 2009. Personalized product recommendations: Predicting shoppers’ needs.
SHARMA, A. and YAN, B. 2013. Pairwise learning in recommendation: Experiments with community recommendation on linkedin. In ACM Conf. on Recommender Sys.