Abstract
Abstract—A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. content-based filtering. Therefore, the systems should come with predeter-mined information either by users or by movies. One interesting research question should be asked: “what if this information is missing or not manually manipulated?” The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies’ genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users’ profiles, our approach has eliminated the effect of the cold-start problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute ade-quate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.
Highlights
Recommender systems (RSs) have been developed to generate meaningful recommendations any products or items to a group of users that might get their attention
The prevalence of movie recommendation systems has been an indispensable component in a wide range of websites and e-commerce applications
Tag usability is increasing in many recommendation systems, yet appropriate algorithms are available to exploit these tags
Summary
Recommender systems (RSs) have been developed to generate meaningful recommendations any products or items to a group of users that might get their attention. Many real world examples of recommendation operation can be found for books on Amazon [7], music on Spotify [8], activities on social media [9], [10], services on Twitter [11], [12], or movies on Netflix [13] The design of these systems depends on the particular characteristics of the datasets, e.g. the ratings of 1 (most disliked) to 5 (most liked). The systems might incorporate other information such as descriptions, multimedia contents, and demographic knowledge Such data sources capture the interactions between items-items, usersusers, and users-items. GroupLens research group developed MovieLens as an online movie recommendation system that allows users to rate movies and integrates rating from different sources to collaboratively recommend to other people. To the best of our knowledge, the research on a movie recommender system based on tags has never been done on the MovieLens 100K and MovieLens 1M variations
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