get right.
Traditional marketing endeavors target relevant consumers through segmentation practices. However, demographic and psychographic composition of audiences vary too much from movie to movie to be able to target consumers effectively in the possible time-span (Hixson, 2005). As trailers are usually released in a one-size-fits-all manner, the clips used in these advertisements are selected to appeal to a wide audience. Due to time and budget constraints, catering different movie trailers to different audience segments would be difficult task.
With the shift of content to the digital realm, many new avenues for advertising are opening up. One highly notable development is the rise of personalized experiences, with brands that are taking advantage of the opportunities of personalization seeing revenues increase two to three times faster than those that do not (BCG, 2017). Whereas segmentation seems to be based on the problem that each consumer cannot be targeted individually, personalization may counter this rhetoric altogether. In an industry in which large sums of money are spent on one-size-fits-all advertising, personalization of trailer advertisements may help studios target consumers on an individual level.

1.2 Research question
Given the importance of movie trailers in the consumer decision process and the large budget all, it is relevant to research how to make this advertising medium more relevant to consumers personally. Bigger, more relevant datasets mean that there is more insight to consumer preferences, which provides ample opportunity to target audiences highly specifically. This thesis proposes a personalized movie trailer based on such consumer preferences. Such a system would help movie studios to target different audiences more effectively, as each consumer will be shown a movie trailer highly relevant to their own personal preferences. For instance, if data shows that user X will watch anything that features actor Y, a trailer that heavily features that actor should be more effective in persuading that user to watch the movie.
Central to linking consumer preferences data to movie trailers are recommendation systems. Recommendation systems (RS) are software tools and techniques that provide suggestions for items that are most likely to be of interest to a user (Ricci, Rokach, & Shapira, 2015). Content-based RSs generate recommendations by combining user feedback on items with the content (i.e., features) associated with them (Lops, De Gemmis, & Semeraro, 2011). Operating by the logic that users will prefer the same features in movies as they do in movie trailers, the following research question is proposed: