Ing. Stanislav Kuznetsov, Ph.D.

Article

10.1007/978-3-031-42941-5_52

Department of Software Engineering
Department of Applied Mathematics

Many recommendation systems struggle with the cold-start problem, especially in the early stages of a new application, when there are few active users and limited interactions. Traditional approaches like Collaborative Filtering cannot provide enough recommendations, and text-based methods, while helpful, do not offer sufficient context. This paper argues against the idea that the cold-start phase will eventually disappear and proposes a solution to enhance recommendation performance from the start. We propose using Ontologies and Knowledge Graphs to add a semantic layer to text-based methods and improve the recommendation performance in cold-start scenarios. Our approach uses ontologies to generate a knowledge graph that captures item text attributes’ implicit and explicit characteristics, extending the item profile with similar semantic keywords. We evaluate our method against state-of-the-art text feature extraction techniques and present the results of our experiments.

Article

10.1007/978-3-031-42519-6_21

Department of Software Engineering
Department of Applied Mathematics

Modern technical universities help students get practical experience. They educate thousands of students and it is hard for them to connect individual students with relevant industry experts and opportunities. This article aims to solve this problem by designing a matchmaking procedure powered by a recommendation system, an ontology, and knowledge graphs. We suggest improving recommendations and reducing the cold-start problem with a re-ranking module based on student educational profiles for students who opt-in. Each student profile is represented as a knowledge graph derived from the successfully completed courses of the individual. The system was tested in an online experiment and demonstrated that recommendations based on student educational profiles and their interaction history significantly improve conversion rates over non-personalised offers.

Proceedings paper

10.1109/CSIT56902.2022.10000782

Department of Applied Mathematics
Faculty of Information Technology

Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder (EASE). We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.

Proceedings paper

10.1109/IJCNN.2016.7727600

Department of Theoretical Computer Science
Department of Software Engineering

Educational data can help us to personalise university information systems. In this paper, we show how educational data can be used to improve the performance of interaction-based recommender systems. Educational data is transformed to student profiles helping to prevent cold start problems when recommending projects to students with few user interactions. Our results show that our hybrid interaction based recommender boosted by educational profiles significantly outperforms bestseller recommendation, which is a mainstream recommendation method for cold start users.

Proceedings paper

10.1007/978-3-319-19713-5_54

Department of Theoretical Computer Science

We are focusing on an issue regarding how to actually recognize the skills of students based on educational results. Existing approaches do not offer suitable solutions. This paper will introduce algorithms making possible to aggregate educational results using ontology. We map the aggregated results, using various methods, as skills that are understandable for external partners and usable to recommend students for projects and projects for students. We compare the results of individual algorithms with subjective assessments of students, and we apply a recommendation algorithm that closely models these skills.

Proceedings paper

Department of Theoretical Computer Science

Universities are seldom using their data efficiently. In this case study, we show how educational data can be used to recommend suitable students for project, get feedback from industrial partners, help students to focus on skills that are demanded by companies. We have developed portal for students collaboration with industrial partners and run it in a pilot for almost a year. Based on our observations described in this contribution, we are adjusting the portal to enhance the functionality and streamline processes supported by the portal.