What Makes a Good Movie Recommendation? Feature Selection for Content-Based Filtering

Gawinecki, Maciej; Szmyd, Wojciech; Żuchowicz, Urszula; Walas, Marcin

doi:10.1007/978-3-030-89657-7_21

Cited by 4 publications

(5 citation statements)

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“…These systems use various approaches of the machine and deep learning [5,6], matrix completion or factorization [7][8][9][10][26][27][28], and lately, GNNs to recommend movies [3,[11][12][13][14]. In the following, we divide these approached into three categories: collaborative filter [1,2,4,8,9,15,16], content filter [4,17,19,22], and hybrid [20,21,29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The cold start problem can lead to recommending movies based on popularity, and on the other hand, for new users, poor recommendations and user dissatisfaction [4,17]. The second category of movie recommender systems is the content-based filtering method [4,17,19,22], which recommends a movie to a user based on the content of the movies that he had watched in the past. If a user watched or searched for comedy movies in the past, it is more likely for the user to continue doing this behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus these methods would also suggest other comedy movies. This example is based on finding similar movies on the basis of the genres of the movies, but there are other similarity metrics as well, such as using tags created by users, summary or subtitles, style and mood of movies, characters list, and movie directors [17,19]. In previous papers, similarity has been measured based on features such as director, actors, and genre of movies, but the most popular feature is the textual content of movies, such as summary, plot, and subtitles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, recent recommenders strive to recommend movies more customized to each user. Therefore, various machine and deep learning models [5,6], matrix completion and factorization [7][8][9][10], and GNN model [3,[11][12][13][14] have been introduced to produce informative recommender systems, which can be divided into three categories of collaborative filter [15][16][17][18], content-based filter [17,19], and hybrid [4,20,21].…”

Section: Introductionmentioning

confidence: 99%

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MoRGH: Movie Recommender System using GNNs on Heterogeneous Graphs

Ziaee,

Rahmani,

Nazari

2024

Preprint

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Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches, and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, cold start problem, and neglecting valuable information hidden when the data is presented in a heterogeneous graph like movie-movie or user-user information. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies’ plot synopsis and genres. Second, constructs a heterogeneous graph of users and movies by taking into account the movies’ rating information available in the Movielens dataset. Third, mitigates the drawbacks of previous methods by performing a GNN and GAE-based model to exploit a hybrid approach of collaborative and content-based approaches. Finally recommends, accurate and more personalized recommendations for each user, which outperforms previous state-of-the-art models w.r.t RMSE scores with an average of 0.1 based on movie ratings.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MoRGH: Movie Recommender System using GNNs on Heterogeneous Graphs

Ziaee,

Rahmani,

Nazari

2024

Preprint

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“…Several recent works utilize USE to represent textual items in various recommendation system domains [52], [53]. Moreover, in a recent study, Gawinecki et al [54], find USE to be the best content-based representation method for producing movie recommendations.…”

Section: Comparative Approachesmentioning

confidence: 99%

Normalized Attraction Travel Personality Representation for Improving Travel Recommender Systems

Alenezi

Hirtle

2022

IEEE Access

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Travel recommender systems (TRSs) aim to reduce travel-related search overload. A significant part of a TRS is representing attractions in a way that reflect the explicit and implicit features of attractions. However, traditional attraction representation methods may not provide a complete image of attractions. Building on the notions of user travel styles (UTSs) and the wisdom of crowds, we propose a method derived from topic-model-based models to represent travel attractions, called the Normalized Attraction Travel Personality (NATP) representation. This approach attempts to leverage the semantics of attraction reviews to model user travel personalities (UTPs), which collectively can construct the attraction travel personality (ATP) representation. Furthermore, we regularize and normalize the ATP representation to obtain our proposed representation. This NATP-based attraction representation could capture implicit characteristics of attractions revealed by the wisdom of crowds. Our experiments show that our representation method gained better results when evaluated against comparative approaches in terms of rating prediction and recommendation ranking quality, indicating the effectiveness of the proposed attraction representation. Lastly, we qualitatively investigate how our attraction representation surpasses the state-ofthe-art representation methods.INDEX TERMS Content-based filtering, attraction representation, knowledge discovery, travel styles, travel recommender systems.

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