Chamani, Houmaan2024-11-072024-11-072024-11-072024-10-28https://hdl.handle.net/10012/21175Recommendation systems have long been evaluated by collecting a large number of individuals' ratings for items, and then dividing these ratings into test and train sets to see how well recommendation algorithms can predict individuals' preferences. A complaint about this approach is that the evaluation measures can only use a small number of known preferences and have no information about the majority of recommended items. Prior research has shown that offline evaluation of recommendation systems using a test/train split methodology may not agree with actual user preferences when all recommended items are judged by the user. To address this issue, we apply traditional information retrieval test collection construction techniques for movie recommendations. An information retrieval test collection is composed of documents, search topics, and relevance judgments that tell us which documents are relevant for each topic. For our test collection, each search topic is an individual who is looking for movies to watch. In other words, while the search topic is always ``Please recommend me movies that I will be interested in watching,'' the context of the search topic changes to be the individual who is requesting the recommendations. When document collections are too large to be completely judged by assessors, the traditional approach is to use pooling. We followed this same approach in the construction of our test collection. For each individual, we used their existing profile of rated movies as input to a wide range of recommendation algorithms to produce recommendations for movies not found in their profile. We then pooled these recommendations separately for each person and asked them to rate the movies. In addition to rating, we also had each individual rate a random sample of movies selected from their ratings profile to measure their consistency in rating. The resulting new test collection consists of 51 individual ratings profiles totaling 123,104 ratings and 31,236 relevance judgments. In this thesis, we detail the creation of the test collection and provide an analysis of the individuals that comprise its search topics, and we analyze the collection's relevance judgments as well as other aspects.enMaster Thesis