09:30 Pre-discussion (attendants: Mehdi Elahi, Benjamin Kille) 

10:00 start explanation followed by thesis presentation (attendants: all) 
Around 10:40 Interrogation (attendants: Alvsvåg, Mehdi Elahi, Benjamin Kille)

Around 11:15 Consultation between censors (attendants: Mehdi Elahi, Benjamin Kille)

Around 11:35: Results (attendants: all)

Title: Addressing the Next-Poster Problem: A Hybrid Recommender System for Streaming Platforms

Abstract:

Recommendation strategies in the Movie domain is varied, but has been shown to aid users in finding content they like. On video streaming-platforms such as TV 2 Play, the user is exposed to several different arenas where they can find something they would like to watch, be that the landing-page of the streaming site, or a suggestion for something more to watch after they concluded a movie or series. These are all areas where Recommender strategies can recommend something based on either the preferences of the user, or in the case of the concluded movie or series, something more to watch based on what they just watched. This latter aspects, being what I refer to as the next-poster problem in this thesis, is not a largely explored area of research, where previous actors have simply utilized the already established Collaborative Filtering (CF) model concerned with the user’s preferences without considering what the user just watched. Here I show that a solution to the next-poster problem is to combine the CF model with a Sequence Aware approach based on Markov Chains, finding an increase in implied user satisfaction over the baseline CF approach. Through an online evaluation on the streaming platform TV 2 Play, I show that using a Hybrid approach to solve the next-poster problem rather than a traditional CF model leads to a lessening in user engagement such as CTR, but an increase in the clicks resulting in a user actually watching the content, this being our implied user satisfaction. Further as a result of this online evaluation, I am able to show that its possible to find the best configuration for a Hybrid model based on Sequence Aware and CF approaches deployed in a real life scenario, through offline evaluation. The results allows me to showcase the importance of considering Sequence of items when recommending for the next-poster problem, and to show that an offline evaluation can imply results in a real world scenario, when considering the Movie domain. Although an improvement, this thesis also shows that there are many more avenues to consider for the next-poster problem.