TV users have an abundance of different movies they could choose from, and with the quantity and quality of data available both on user behavior and content, better recommenders are possible. In this paper, we evaluate and combine different content-based and collaborative recommendation methods for a Turkish movie recommendation system. Our recommendation methods can make use of user behavior, different types of content features, and other users' behavior to predict movie ratings. We gather different types of data on movies, such as the description, actors, directors, year, and genre. We use natural language processing methods to convert the Turkish movie descriptions into keyword vectors. Then, for each user, we use the content features and the user's past implicit ratings to produce content feature-based user profiles. In order to have more reliable profiles, we do feature selection on these profiles. We show that for each feature space, such as actor, director, or keyword, a different amount of feature selection may be optimal. Different recommenders may also perform best for a different number of movies available as training data for a user. We also combine different content-based recommenders and collaborative recommenders using an aggregation or the best of the available recommenders. Experimental results on a dataset with hundreds of users and movies show that, especially for users who have watched a small number of movies in the past, feature selection can increase recommendation success.
ÖZETÇEBeraber öneri sistemlerinde, genellikle, kullanıcıların sevdiği ve sevmediği ürünleri açık olarak derecelendirdiği dolaysız geri bildirimli öneri yöntemleri kullanılmaktadır. Öte yandan, TV program önerisi gibi çoğu alanda kullanıcıdan her program için derecelendirme istemek zordur. Derecelendirme yerine, kullanıcıların hangi ürünleri ne kadar süre ile izlediği bilgisinin toplanması ve dolaylı geri bildirimli öneri yöntemlerinin kullanılması daha uygundur. Bu çalışmada, TV programı izleme sürelerinden üretilen derecelendirme verileri ve normalize edilmiş izleme süreleri kullanılarak yapılan TV program önerisi performansları karşılaştırılmıştır. Öneri yöntemi olarak dolaysız geri bildirimli öneri yöntemleri düzenli matris çarpanlarına ayırma yöntemi ile beraber kullanılmıştır. Matris çarpanlarının öğrenilmesi sırasında hem öğrenmenin hızlandırılması için uyarlamalı öğrenme hızı kullanılmış, hem de kullanıcı ve ürüne uyarlamalı düzenleme yöntemleri kullanılmıştır. ABSTRACTMost collaborative filtering methods use explicit ratings given by users for some items. On the other hand, as in TV program recommendation, sometimes it is not easy to obtain those explicit ratings. Instead of ratings, there is information on how long the user watched an item. For such problems, instead of explicit recommendation methods, implicit methods should be used. In this paper we process the time durations for which users watch the programs to obtain rankings and also use the normalized time durations for TV program recommendation. As the recommendation methods, we use explicit recommendation methods together with matrix factorization and relate these methods with the implicit recommendation methods. While learning the matrix factors, we introduce adaptive learning rate to speed up the learning and we also introduce user/item adaptive regularization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.