Personalized Ranking of Movies: Evaluating Different Metadata Types and Recommendation Strategies
نویسندگان
چکیده
This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders.
منابع مشابه
Optimal Ranking for Video Recommendation
Item recommendation from implicit feedback is the task of predicting a personalized ranking on a set of items (e.g. movies, products, video clips) from user feedback like clicks or product purchases. We evaluate the performance of a matrix factorization model optimized for the new ranking criterion BPR-Opt on data from a BBC video web application. The experimental results indicate that our appr...
متن کاملMultimodal Content Representation and Similarity Ranking of Movies
In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content. In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining. We emphasize our research in topic modeling of movies based on their subtitles. In order to demonstrate the proposed content ...
متن کاملBPR: Bayesian Personalized Ranking from Implicit Feedback
Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are des...
متن کاملExploiting Social Tags in Matrix Factorization Models for Cross-domain Collaborative Filtering
Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) from other source domains. Due to the heterogeneity of item characteristics across domains, content-based recommendation methods are difficult to apply, and collaborative filtering has become the most popular approach to cross-domain recom...
متن کاملRBPR: Role-based Bayesian Personalized Ranking for Heterogeneous One-Class Collaborative Filtering
Heterogeneous one-class collaborative filtering (HOCCF) is a recently studied important recommendation problem, which consists of different types of users’ one-class feedback such as browses and purchases. In HOCCF, we aim to fully exploit the heterogenous feedback and learn users’ preferences so as to make a personalized and ranking-oriented recommendation for each user. For HOCCF, we can appl...
متن کامل