title: ÐÐ½Ð°Ð»Ð¸Ð· Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ Ð¾ Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ð¸ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Analysis of personalized recommendation algorithms based on user behavior data
Creators: name: Ð¤Ð°Ð½Ñ ÐÐ½Ð³Ð°Ð½
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: 2022
Subjects: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ
ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð°Ñ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ñ
Ð±Ð¾Ð»ÑÑÐ¸Ðµ Ð´Ð°Ð½Ð½ÑÐµ
ÐºÐ¾Ð»Ð»Ð°Ð±Ð¾ÑÐ°ÑÐ¸Ð²Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
recommendation system
e-commerce
big data
collaborative filtering

description: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ ÑÐ°Ð·Ð²Ð¸Ð²Ð°ÑÑÑÑ Ð±ÑÑÑÑÑÐ¼Ð¸ ÑÐµÐ¼Ð¿Ð°Ð¼Ð¸ Ñ Ð¼Ð¾Ð¼ÐµÐ½ÑÐ° ÑÐ²Ð¾ÐµÐ³Ð¾ Ð¿Ð¾ÑÐ²Ð»ÐµÐ½Ð¸Ñ, Ð° ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ðµ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° ÑÐ±Ð»Ð¸Ð·Ð¸Ð»Ð¾ Ð»ÑÐ´ÐµÐ¹. Ð¡Ð¾ Ð²ÑÐµÐ¼ÐµÐ½ÐµÐ¼ Ð´Ð°Ð½Ð½ÑÐµ Ð² ÐÐ½ÑÐµÑÐ½ÐµÑÐµ ÑÑÐ°Ð½Ð¾Ð²ÑÑÑÑ Ð²ÑÐµ Ð±Ð¾Ð»ÐµÐµ ÑÐ»Ð¾Ð¶Ð½ÑÐ¼Ð¸, Ð¸ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ Ð¿ÐµÑÐµÐ³ÑÑÐ·ÐºÐ¸ ÑÑÐ°Ð½Ð¾Ð²Ð¸ÑÑÑ Ð²ÑÐµ Ð±Ð¾Ð»ÐµÐµ ÑÐµÑÑÐµÐ·Ð½Ð¾Ð¹. ÐÑÐ¾Ð±Ð»ÐµÐ¼Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ Ð¿ÐµÑÐµÐ³ÑÑÐ·ÐºÐ¸ ÑÑÐ°Ð½Ð¾Ð²Ð¸ÑÑÑ Ð²ÑÐµ Ð±Ð¾Ð»ÐµÐµ ÑÐµÑÑÐµÐ·Ð½Ð¾Ð¹, Ð¸ Ð²ÑÐ·ÑÐ²Ð°ÐµÑ Ð¾Ð·Ð°Ð±Ð¾ÑÐµÐ½Ð½Ð¾ÑÑÑ, ÐºÐ°Ðº Ð½Ð°Ð¹ÑÐ¸ Ð¿Ð¾Ð»ÐµÐ·Ð½ÑÑ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð² Ð¾Ð³ÑÐ¾Ð¼Ð½Ð¾Ð¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ðµ Ð´Ð°Ð½Ð½ÑÑ. Ð ÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¸Ð´ÑÐ¼Ð°Ð½Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ ÑÑÐ¾Ð¹ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ, Ð¸ Ð½Ð° Ð¿ÑÐ¾ÑÑÐ¶ÐµÐ½Ð¸Ð¸ Ð¼Ð½Ð¾Ð³Ð¸Ñ Ð»ÐµÑ Ð¾Ð½Ð¸ ÑÐ°Ð·Ð²Ð¸Ð²Ð°Ð»Ð¸ÑÑ Ñ Ð±Ð¾Ð»ÑÑÐ¸Ð¼ ÑÑÐ¿ÐµÑÐ¾Ð¼ Ð² ÑÐ°ÐºÐ¸Ñ Ð¾Ð±Ð»Ð°ÑÑÑÑ, ÐºÐ°Ðº ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ñ ÑÐ¸Ð»ÑÐ¼Ð¾Ð², ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ñ Ð¼ÑÐ·ÑÐºÐ¸, ÑÐ¾ÑÐ¸Ð°Ð»ÑÐ½ÑÐµ ÑÐµÑÐ¸ Ð¸ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð°Ñ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ñ.Â ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ ÑÐ²Ð»ÑÑÑÑÑ ÐºÑÐ°ÐµÑÐ³Ð¾Ð»ÑÐ½ÑÐ¼ ÐºÐ°Ð¼Ð½ÐµÐ¼ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼, Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»Ð¸ Ð¿ÑÐ¸Ð´ÑÐ¼Ð°Ð»Ð¸ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð¾ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ Ð´Ð»Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÑÐµÐ½Ð°ÑÐ¸ÐµÐ² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹. ÐÐ´Ð½Ð°ÐºÐ¾ Ð½Ð¸ Ð¾Ð´Ð¸Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð½Ðµ Ð¼Ð¾Ð¶ÐµÑ ÑÐµÑÐ¸ÑÑ Ð²ÑÐµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ. ÐÐ±ÑÐµÐ´Ð¸Ð½ÑÑ ÑÐ¸Ð»ÑÐ½ÑÐµ ÑÑÐ¾ÑÐ¾Ð½Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð¸ Ð´Ð¾Ð¿Ð¾Ð»Ð½ÑÑ Ð¸Ñ ÑÐ»Ð°Ð±ÑÐµ ÑÑÐ¾ÑÐ¾Ð½Ñ, Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð°Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ñ Ð¼Ð¾Ð¶ÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ ÑÐ¼ÑÐ³ÑÐ¸ÑÑ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸.Â Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¸ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð° ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð¾Ð¹ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð±Ð¾Ð»ÑÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð½Ð° Ð¸Ð´ÐµÐµ Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð¾Ð¹ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸. Ð Ð°Ð±Ð¾ÑÐ° ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð° Ð½Ð° Ð´Ð²Ðµ ÑÐ°ÑÑÐ¸: Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ, Ð²Ð¾-Ð¿ÐµÑÐ²ÑÑ, Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ðº ÑÐ¸ÑÑÐµÐ¼Ðµ Ð¸ Ð¾Ð±ÑÐ°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼Ñ.
description: Since the birth of information technology, it has been known to maintain a trend of rapid development, and the development of Internet technology has made the relationship between people closer. As time goes by, the data in the network becomes more and more complicated, and the problem of information overload becomes more and more serious. How to obtain useful information from huge amounts of data has become a problem that people pay attention to. The recommendation system was invented to solve this problem. After years of development, the recommendation system has had relatively successful experience in areas including movie recommendation, music recommendation, social networking, and e-commerce.Â The recommendation algorithm is the cornerstone of the recommendation system. At present, researchers have proposed a variety of different recommendation algorithms to adapt to different recommendation scenarios. However, no recommendation algorithm can solve all the problems. Mixed recommendation can effectively alleviate the defects of a single recommendation method by combining the strengths of multiple recommendation methods and taking advantage of the shortcomings.Â Based on a mixed recommendation idea, this paper designs and implements an e-commerce recommendation system based on big data technology. The work content is divided into two parts: design and implementation. First, the system needs to be analyzed, and the overall architecture of the system is designed.