title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° Ð² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ
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: Application of natural language processing methods in recommender systems
Creators: name: Ð¨ÑÐ»ÑÐ³Ð¸Ð½, Ð¡ÐµÑÐ³ÐµÐ¹
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: 2024
Subjects: ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ñ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹
Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ðµ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð¾ ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³Ð¾Ð²
Doc2Vec
K-Medoids
ÐºÐ¾ÑÐ¸Ð½ÑÑÐ½Ð¾Ðµ ÑÑ Ð¾Ð´ÑÑÐ²Ð¾
recommender system
vectorizing of user preferences
user embeddings space
cosine similarity

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ´ÑÐ° ÐºÐ¾Ð½ÐºÑÑÐµÐ½ÑÐ½Ð¾ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾Ð¹ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ñ ÑÑÑÑÐ¾Ð¼ Ð¿Ð¾ÑÐ»ÐµÐ´Ð½Ð¸Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ð¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ, Ð²Ð¾Ð·Ð½Ð¸ÐºÐ°ÑÑÐ¸Ðµ Ð¿ÑÐ¸ Ð¸Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÐµ Ð¿ÑÑÐ¸ Ð¸Ñ ÑÐµÑÐµÐ½Ð¸Ñ. ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼. Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ñ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹ Pref2Vec ÐºÐ°Ðº Ð¾ÑÐ½Ð¾Ð²Ð° Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð¾Ð¹ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ. ÐÐ¾Ð´ÐµÐ»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð¾Ð´Ð½Ð¾ Ð¸Ð· Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð°ÐºÑÑÐ°Ð»ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° â Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°ÑÑÐ¸ÑÑÐµÑ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Gensim. Ð ÑÐ¾Ð¶Ðµ Ð¶Ðµ Ð±ÑÐ» Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ ÐºÐ»Ð°ÑÑ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ð° ÑÐ¼Ð±ÐµÐ´Ð´Ð¸Ð½Ð³Ð¾Ð² UES, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð¿ÑÐ¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Pref2Vec Ð¸ ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð¾Ð²Ð°ÑÑ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÐµÑÐ½Ð¾Ð³Ð¾ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ Ð¿Ð¾ÑÑÐµÐ´ÑÑÐ²Ð¾Ð¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½Ð¾Ð²Ð¾Ð³Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð° Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐºÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ ÑÑÐ¸ÑÑÐ²Ð°ÐµÑÑÑ Ð²Ð¸Ð´ Ð²Ð·Ð°Ð¸Ð¼Ð¾Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÐ¼Ð¸ Ð´Ð°Ð½Ð½ÑÐ¼Ð¸. Ð¢ÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¼ÐµÑÑÐ¸Ðº Recall@k Ð¸ NCDG@k Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸, ÑÑÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Pref2Vec ÑÑÐ°Ð²Ð½Ð¸Ð¼Ñ Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ¼Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ BPRMF, GRU4Rec Ð¸ NextItRec, ÑÑÐ¾ Ð³Ð¾Ð²Ð¾ÑÐ¸Ñ Ð¾ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¯Ð Ð¡.
description: The purpose of the thesis is to analyze the various machine learning methods in the development of recommender systems and to develop the core of a competitive recommender system. Main aspects of the recommender system development were considered: methods for constructing recommender systems, problems of its implementation, ways to solve the problems and ways to apply machine learning methods in the development. As one of the results, a model of vectorizing user preferences Pref2Vec was proposed as the basis of a hybrid recommender system. The model uses one of the most current methods of natural language processing â vectorization, and also expands the functionality of the Gensim library. In addition, a user space embedding class UES was proposed, which is used in building of the Pref2Vec model and which allows to vectorize end-user preferences by a new method of vectorizing text data, which allows to consider the type of user interaction with the data. Testing of the Pref2vec and comparative analysis of the Recall@k and NCDG@k metrics indicate that the achieving quality of the Pref2Vec model is comparable with the baselines of the BPRMF, GRU4Rec and NextItRec models, which indicates the prospects of the developed core of a recommender system.