Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests

Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning

Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item's descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators

METHOD OF FORMING RECOMMENDATIONS USING TEMPORAL CONSTRAINTS IN A SITUATION OF CYCLIC COLD START OF THE RECOMMENDER SYSTEM

The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests

Rating Prediction with Contextual Conditional Preferences

Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start system scenario, based on user interests model called contextual conditional preferences. We present results obtained with three publicly available data sets in comparison with several state-of-the-art baselines. We show that usage of contextual conditional preferences improves the prediction accuracy, even when all users have provided a few feedbacks, and hence small amount of data is available

Використання нечіткої логіки у процесі експертного оцінювання електронних навчальних ресурсів

The paper deals with expert evaluation of eLearning resources based on the theory of fuzzy logic using the method of hierarchy analysis. The concept of fuzzy logic was used to quantify qualitative data in real decision-making tasks. The authors propose to develop a recommender system based on fuzzy logic methods for expert evaluation of eLearning resources, including computer mathematics systems and for deciding on the selection of the most effective ones for use in the educational process. In the course of research the concept of recommender systems is considered and analyzed according to the field of application. The concept of the recommender system was introduced to support decision-making on the selection of the most effective eLearning resources. A literature review on expert evaluation, on using of fuzzy logic methods and recommender systems in the decision-making process is presented. The general structure of the recommender system of decision support with the description of all subsystems is given as well. The basic information of the theory of fuzzy logic concerning the decision-making process is described. The practical use of fuzzy logic theory in the process of choosing computer mathematics systems is considered. The main criteria for evaluating computer mathematics systems are given. The method of pairwise comparisons was used to calculate the importance of the criteria. The process of evaluating eLearning resources using fuzzy logic methods is described in detail and the algorithm of this approach is given. As a result of the expert assessment, a list of recommended alternatives to eLearning resources that meet the set criteria was obtained. The general structure of the recommender system of decision support for the selection of eLearning resources is given.Розглянуто процедури експертного оцінювання для визначення якості електронних навчальних ресурсів, що базуються на теорії нечіткої логіки та використанні методу аналізу ієрархій. Концепцію нечіткої логіки використано для кількісної оцінки якісних даних для генерування рекомендацій. Запропоновано проєкт рекомендаційної системи на підставі методів нечіткої логіки для експертного оцінювання електронних навчальних ресурсів, зокрема систем комп'ютерної математики та генерування рекомендацій щодо вибору найефективніших для використання в навчальному процесі. Розглянуто та проаналізовано поняття рекомендаційних систем залежно від сфери застосування. Введено поняття рекомендаційної системи для вибору найефективніших електронних навчальних ресурсів. Здійснено огляд наукових публікацій щодо застосування експертного оцінювання, використання методів нечіткої логіки та рекомендаційних систем. Наведено загальну архітектуру рекомендаційної системи з описом функціоналу підсистем. Показано основні можливості застосування теорії нечіткої логіки у процесах генерації рекомендацій. Розглянуто приклади практичного використання теорії нечіткої логіки в процесі вибору систем комп'ютерної математики. Наведено основні критерії оцінювання систем комп'ютерної математики. Використано метод парних порівнянь для розрахунку важливості критеріїв. Детально описано процес оцінювання електронних навчальних ресурсів з використанням методів нечіткої логіки та подано алгоритм роботи цього підходу. Внаслідок проведеного експертного оцінювання отримано перелік рекомендованих альтернатив електронних навчальних ресурсів, що відповідають заданим критеріям. Наведено загальну структуру рекомендаційної системи вибору електронних навчальних ресурсів