تخمین علاقه‌ کاربران به ژانرهای فیلم بر اساس شخصیت و ویژگی‌های جمعیت‌شناختی آن‌ها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی فناوری اطلاعات، دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران

2 دانشیار گروه مهندسی فناوری اطلاعات، دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران

چکیده
هدف: پلتفرم‌های پخش آنلاین فیلم با فراهم‌کردن دسترسی به طیف گسترده‌ای از محتوا، نقش مهمی در نحوه انتخاب و تماشای فیلم ایفا می‌کنند. در این میان، پیش‌بینی ترجیحات کاربران نسبت به ژانرهای مختلف می‌تواند به بهبود شخصی‌سازی و عملکرد سیستم‌های توصیه‌گر کمک کند. با توجه به وجود رابطه معنادار میان ویژگی‌های شخصیتی کاربران و علاقه‌مندی آن‌ها به ژانرهای فیلم، هدف این پژوهش، بررسی قابلیت استفاده همزمان از ویژگی‌های شخصیتی و جمعیت‌شناختی برای پیش‌بینی ترجیحات ژانری کاربران است.
روش: در این پژوهش، فیلم‌ها بر اساس ۱۸ ژانر اصلی و کاربران بر اساس ویژگی‌های شخصیتی مدل پنج‌عاملی شامل وظیفه‌شناسی، برون‌گرایی، توافق‌پذیری، روان‌رنجوری و گشودگی به تجربه، همراه با ویژگی‌های جمعیت‌شناختی شامل سن، جنسیت و موقعیت مکانی، توصیف شدند. سپس، برای هر ژانر، مدل‌های مختلف یادگیری ماشین از جمله رگرسیون بردار پشتیبان و جنگل تصادفی برای یادگیری رابطه میان ویژگی‌های کاربران و میزان علاقه آن‌ها به ژانرهای مختلف آموزش داده شدند. مدل‌ها بر روی نسخه‌ای از مجموعه‌داده MovieLens ارزیابی شدند و معیار میانگین خطای مطلق برای مقایسه عملکرد آن‌ها به کار رفت.
یافته‌ها: نتایج نشان داد مدل‌های پیش‌بینی علاقه به ژانرهای درام، اکشن و جنایی، به‌ترتیب با میانگین خطای مطلق 18/0، 21/0 و 22/0، بهترین عملکرد را داشتند. در مقابل، ژانرهای موزیکال و فانتزی با مقادیر 34/0 و 33/0 ضعیف‌ترین نتایج را به خود اختصاص دادند.
نتیجه‌گیری: نتایج نشان می‌دهد که ترکیب ویژگی‌های شخصیتی و جمعیت‌شناختی می‌تواند رویکردی مؤثر برای پیش‌بینی ترجیحات ژانری کاربران باشد و از این اطلاعات می‌توان در راستای بهبود شخصی‌سازی و عملکرد سیستم‌های توصیه‌گر فیلم استفاده کرد.

کلیدواژه‌ها


عنوان مقاله English

Prediction of Users' Movie Genre Preferences Based on Their Personality and Demographic Features

نویسندگان English

Mahsa Rezaei 1
Marjan Kaedi 2
1 Master’s Student, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
2 Associate Professor, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
چکیده English

Purpose: Online movie streaming platforms provide access to a wide range of content and have changed the way audiences select and watch movies. Predicting users’ preferences for different movie genres can improve personalization and the performance of movie recommender systems. Given the significant relationship between users’ personality traits and their preferences for movie genres, this study aims to investigate the effectiveness of using personality and demographic characteristics to predict users’ genre preferences.
Method: In this study, movies were characterized according to 18 main genres, while users were represented by personality traits based on the Five-Factor Model, including conscientiousness, extraversion, agreeableness, neuroticism, and openness to experience, along with demographic characteristics including age, gender, and location. Various machine learning models, including Support Vector Regression and Random Forest Regression, were trained separately for each genre to learn the relationship between user characteristics and their genre preferences. The models were evaluated on a version of the MovieLens dataset using Mean Absolute Error (MAE) as the primary evaluation metric.
Findings: The results showed that the models for predicting preferences for drama, action, and crime achieved the best performance, with MAE values of 0.18, 0.21, and 0.22, respectively. In contrast, the models for musical and fantasy genres obtained the highest error values, with MAEs of 0.34 and 0.33, respectively.
Conclusion: The findings indicate that combining personality and demographic characteristics can effectively predict users’ genre preferences and can provide useful information for improving personalization and the performance of movie recommender systems.

Methods: In this study, movies were characterized according to 18 main genres, while users were represented by personality traits based on the Five-Factor Model, including conscientiousness, extraversion, agreeableness, neuroticism, and openness to experience, along with demographic characteristics including age, gender, and location. Various machine learning models, including Support Vector Regression and Random Forest Regression, were trained separately for each genre to learn the relationship between user characteristics and their genre preferences. The models were evaluated on a version of the MovieLens dataset using Mean Absolute Error (MAE) as the primary evaluation metric.

Findings: The results showed that the models for predicting preferences for drama, action, and crime achieved the best performance, with MAE values of 0.18, 0.21, and 0.22, respectively. In contrast, the models for musical and fantasy genres obtained the highest error values, with MAEs of 0.34 and 0.33, respectively.

Conclusion: The findings indicate that combining personality and demographic characteristics can effectively predict users’ genre preferences and can provide useful information for improving personalization and the performance of movie recommender systems.

کلیدواژه‌ها English

Genre preferences
Machine learning
Personality traits
Big-five personality traits
Modeling
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