title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ñ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a content recommendation system based on user preference analysis
Creators: name: ÐÐ°ÐºÐ°ÑÐ¾Ð², ÐÐ¸ÑÐ°Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÑÐ¸ÑÐ° Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ-Ð¾Ð±ÑÐµÐºÑ
Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°
recommendation system
machine learning
user-item matrix
autoencoder
neural network
content personalization

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ: Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼ Ð¸ Ð¸Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÐµÐ¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÐ¸Ð»ÑÐ¼Ð°Ñ. ÐÐ°Ð´Ð°ÑÐ¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð²ÐµÑÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¸ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼. 2. ÐÐ·ÑÑÐ¸ÑÑ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÐµ Ð² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ. 3. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°ÑÑ Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð²ÐºÐ»ÑÑÐ°Ñ Ð¼Ð°ÑÑÐ¸ÑÐ½ÑÑ ÑÐ°ÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ, Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÑ Ð¸ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸. 4. Ð¡ÑÐ°Ð²Ð½Ð¸ÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐ°Ð¼ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÑÐ°Ð½-Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÐµÑÐ¾Ð´Ñ: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð²ÐºÐ»ÑÑÐ°Ñ Ð½ÐµÐ¾ÑÑÐ¸ÑÐ°ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð°ÑÑÐ¸ÑÐ½ÑÑ ÑÐ°ÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ, Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð¸ Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÑ. ÐÐ½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ MovieLens Latest Datasets. ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð½Ð° ÑÐ·ÑÐºÐµ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐµÐ´Ñ Google Colaboratory. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾, ÑÑÐ¾ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÐ° Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÐµÑ Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐµ NDCG, ÑÑÐ¾ Ð´ÐµÐ»Ð°ÐµÑ ÐµÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐµÐ¹ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ, ÑÐ²ÑÐ·Ð°Ð½Ð½ÑÑ Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼. ÐÐ°ÑÑÐ¸ÑÐ½Ð°Ñ ÑÐ°ÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð»Ð° Ð¼Ð¸Ð½Ð¸Ð¼Ð°Ð»ÑÐ½ÑÐµ Ð¾ÑÐ¸Ð±ÐºÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ (Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐ°Ð¼ MSE Ð¸ MAE), ÑÑÐ¾ Ð³Ð¾Ð²Ð¾ÑÐ¸Ñ Ð¾ ÐµÑ Ð½Ð°Ð´ÑÐ¶Ð½Ð¾ÑÑÐ¸ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÐµÐ¹ÑÐ¸Ð½Ð³Ð¾Ð². ÐÐ¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð¿ÑÐ¾Ð¼ÐµÐ¶ÑÑÐ¾ÑÐ½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ, ÑÑÑÑÐ¿Ð¸Ð² Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÑ Ð² ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ Ð¼Ð°ÑÑÐ¸ÑÐ½Ð¾Ð¹ ÑÐ°ÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð² ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹. ÐÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ°Ñ Ð·Ð½Ð°ÑÐ¸Ð¼Ð¾ÑÑÑ: ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸Ð¼ÐµÑÑ ÑÐ¸ÑÐ¾ÐºÑÑ Ð¾Ð±Ð»Ð°ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼. Ð­ÑÐ¾ Ð²ÐºÐ»ÑÑÐ°ÐµÑ ÑÐ°ÐºÐ¸Ðµ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ, ÐºÐ°Ðº ÑÑÑÐ¸Ð¼Ð¸Ð½Ð³Ð¾Ð²ÑÐµ ÑÐµÑÐ²Ð¸ÑÑ, Ð¸Ð½ÑÐµÑÐ½ÐµÑ-Ð¼Ð°Ð³Ð°Ð·Ð¸Ð½Ñ Ð¸ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ñ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°, ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ð³Ð¾ Ð¾Ð¿ÑÑÐ° Ð¸ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ Ð²Ð¾Ð²Ð»ÐµÑÑÐ½Ð½Ð¾ÑÑÐ¸ Ð°ÑÐ´Ð¸ÑÐ¾ÑÐ¸Ð¸. ÐÐ°ÐºÐ»ÑÑÐµÐ½Ð¸Ðµ: Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð°, ÑÑÐ¾ Ð²ÑÐ±Ð¾Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð·Ð°Ð²Ð¸ÑÐ¸Ñ Ð¾Ñ ÑÐµÐ»ÐµÐ¹ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¸ ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐµÐ½Ð¸Ð¹. ÐÐ°ÑÑÐ¸ÑÐ½Ð°Ñ ÑÐ°ÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð¾Ð²Ð°Ð½Ð° Ð´Ð»Ñ Ð·Ð°-Ð´Ð°Ñ, Ð³Ð´Ðµ Ð²Ð°Ð¶Ð½Ð° ÑÐºÐ¾ÑÐ¾ÑÑÑ Ð¸ Ð¿ÑÐ¾ÑÑÐ¾ÑÐ° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑ Ð´Ð»Ñ ÑÐ»Ð¾Ð¶Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð½Ð¾ ÑÑÐµÐ±ÑÑÑ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸-ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð². ÐÐ¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿Ð¾Ð»ÐµÐ·Ð½Ñ Ð² ÑÐ¸ÑÑÐµÐ¼Ð°Ñ, Ð³Ð´Ðµ ÑÑÐµÐ±ÑÐµÑÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ»Ð¾Ð¶Ð½ÑÑ Ð½ÐµÐ»Ð¸Ð½ÐµÐ¹Ð½ÑÑ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐµÐ¹. ÐÐ°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ñ Ð½Ð° Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ñ Ð³Ð¸Ð±ÑÐ¸Ð´Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ ÑÐµÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ ÑÐ¾Ð»Ð¾Ð´Ð½Ð¾Ð³Ð¾ ÑÑÐ°ÑÑÐ°.
description: Objective: To compare various approaches to building recommendation systems and apply them to predict user preferences based on movie data. Research tasks: 2) Analyze the subject area and existing solutions in the field of recommen-dation systems. 3) Study the main machine learning algorithms used in recommendation systems. 4) Implement and test several models, including matrix factorization, auto-encoders, and fully connected neural networks. 5) Compare model performance using accuracy metrics (MSE and MAE) and ranking metrics (NDCG). Methods: The study utilized modern machine learning methods: non-negative matrix factorization, autoencoders, and fully connected neural networks. The analysis was conducted using the MovieLens Latest Datasets, which includes millions of user ratings and movie metadata. The implementation was carried out in Python using the Google Colaboratory environment. Results: The research demonstrated that the autoencoder model achieves the best results according to the NDCG metric, making it the most suitable for ranking tasks. Matrix factorization showed minimal prediction errors (MSE and MAE metrics), indicating its reliability for rating prediction tasks. The fully connected neural network produced intermediate results, underperforming the autoen-coder in ranking and matrix factorization in prediction accuracy. Practical significance: The obtained results have a broad application scope in the development and optimization of recommendation systems. These include areas such as streaming services, ecommerce platforms, and educational resources. The research findings can enhance content personalization, improve user experience, and increase audience engagement. Conclusion: The study revealed that the choice of model depends on the application objectives and technical constraints. Matrix factorization is recommended for tasks prioritizing speed and simplicity. Autoencoders are suitable for complex ranking tasks but require additional computational resources. Fully connected neural networks are beneficial for systems that require analyzing complex non-linear relationships. Future research could focus on integrating hybrid methods and addressing the cold-start problem.