Genre based hybrid filtering for movie recommendation engine

Roy, Arighna; Ludwig, Simone A.

doi:10.1007/s10844-021-00637-w

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Roy et al 10 proposed that by using hybrid filtering, a new movie's genre is used to address this issue. GAHF and GSHF exhibit lower MAEs than item-item collaborative filtering of 18% and 23%, respectively.…”

Section: Movies and Video Genre Classificationmentioning

confidence: 99%

“…Based on commonalities in the story components or viewers' emotional reactions, the term "genre" categorise a film 9 . As many researchers explain, a film is created by subject experts, and the genre is a trustworthy content indicator for the film 10 . Movie genres are crucial aspects of films since they can be used as tags for targeted searches on multimedia platforms and to create individualised movie recommendations 11 .…”

Section: Introductionmentioning

confidence: 99%

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Hindi Podcast Genre Prediction using Support Vector Classifier

Mahrishi

Jain

Sharma

2023

Preprint

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India sees a 23% rise in podcast listening after the Covid-19 pandemic. The pandemic and screen fatigue led people to seek out old favourite audio podcasts. Podcast genre classification allows listeners to compile a playlist of their favourite tracks; this also helps podcast streaming services provide recommendations to users based on the genre of the podcasts they enjoy. After the COVID-19 pandemic, the need for educational content in all forms, including podcasts, has skyrocketed, making it even more crucial to anticipate the genre of educational podcasts. Educational podcasts are a sub-genre of the broader education genre and typically involve audio recordings of discussions, lectures, or interviews on educational topics. Education podcast genre prediction is required to efficiently classify and arrange educational content and make it simpler for listeners to access and absorb pertinent information. This study focuses on Podcast Genre Prediction, specifically for the Hindi language. In our study, our developed PodGen dataset is used, which consists of 550 podcasts of 5 minutes each and have a total of 26,867 sentences, where every podcast has been manually annotated into one of the four genre categories (Horror, Motivational, Crime, and Romance). The performance comparison of state-of-the-art machine learning techniques on the PodGen dataset is used to demonstrate accuracy. The best performance was observed in the case of the Support Vector Classifier model with balanced accuracy:82.42%, precision (weighted):83.09%, recall (weighted):82.42%, and F1 score(weighted):82.39% on testing data.

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Section: Movies and Video Genre Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hindi Podcast Genre Prediction using Support Vector Classifier

Mahrishi

Jain

Sharma

2023

Preprint

View full text Add to dashboard Cite

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Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain

Musto,

Martina,

Iovine

et al. 2023

J Intell Inf Syst

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Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users' interests and needs range from button-based interfaces, where users have to select what they like among a set of fixed alternatives, to more recent conversational interfaces, where users have to reply to some questions formulated by the algorithm about their preferences. However, none of these strategies either mimics the dynamics of real-world open-ended interactions, or allows users to express their needs with an adequate level of expressiveness and control. In this paper, we present a strategy that allow users to express their preferences and needs through natural language statements. In particular, our natural language preference elicitation pipeline allows users to express preferences on objective movie features (e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining user-written movie reviews. Natural Language Preference Elicitation in the Movie DomainTo validate our claims, we carried out a user study in the movie domain (N = 249). The main finding of our experiment is that users tend to express their preferences by using objective features, whose usage largely overcomes that of subjective features, that are more complicated to be expressed. However, the combination of objective and subjective features often leads to better recommendations, at the cost of a slightly longer conversation. We have also identified the main challenges that arise when users talk to the virtual assistant by using subjective features, and this paves the way for future developments of our methodology.

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