A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm’s input parameters used in the recommender system to improve the accuracy of predictions with less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the ‘MovieLens 100k dataset’. The proposed system can be generalized to other domains as well.
|Title of host publication||2018 Thirteenth International Conference on Digital Information Management (ICDIM)|
|Number of pages||5|
|Publication status||Published - Sept 2018|
|Event||International Conference on Digital Information Management 2018 (ICDIM) - Berlin, Germany|
Duration: 24 Sept 2018 → 26 Sept 2018
|Conference||International Conference on Digital Information Management 2018 (ICDIM)|
|Period||24/09/18 → 26/09/18|