Ontologie v rekomendačních systémech

Abstract

The cold-start problem is typical in recommender systems (RS). There are several ways to reduce this problem. There are naive approaches, or there are algorithms that try to reduce the problem by using attributes or generating additional information. We find a way to automatically or semi-automatically generate more in-depth knowledge of the domain and use that knowledge to minimise the cold-start problem. This knowledge is, in our case, represented by a semantic level that can add essential information about items and also be understandable to users. Our approach is to use ontology in the form of a graph for semantic purposes. We use the benefits of graph architecture to automatically generate an ontological profile for the items, and if needed, we can extend profiles with the most similar semantic nodes, and use these profiles for recommendation purposes. In this thesis, we describe the principle of automatically generating ontologies and improving them with explicit word embeddings. We present the general methodology for evaluating algorithms for cold-user and cold-item problems. Next, we show our ontology-based algorithms for reducing the cold-start problem (OBACS). The algorithm is a combination of ontology, and collaborative filtering (CF) approaches. It is a universal algorithm that could reduce the cold-start problem in an earlier phase where more of the items are cold, and also in the transition phase, where the number of cold-start items decreases. Also, these algorithms could be used for diversification and to help CF approaches with the “filter bubble” problem. We prove our approach with an offline experiment in the area of movie recommendation and discuss the results.

The cold-start problem is typical in recommender systems (RS). There are several ways to reduce this problem. There are naive approaches, or there are algorithms that try to reduce the problem by using attributes or generating additional information. We find a way to automatically or semi-automatically generate more in-depth knowledge of the domain and use that knowledge to minimise the cold-start problem. This knowledge is, in our case, represented by a semantic level that can add essential information about items and also be understandable to users. Our approach is to use ontology in the form of a graph for semantic purposes. We use the benefits of graph architecture to automatically generate an ontological profile for the items, and if needed, we can extend profiles with the most similar semantic nodes, and use these profiles for recommendation purposes. In this thesis, we describe the principle of automatically generating ontologies and improving them with explicit word embeddings. We present the general methodology for evaluating algorithms for cold-user and cold-item problems. Next, we show our ontology-based algorithms for reducing the cold-start problem (OBACS). The algorithm is a combination of ontology, and collaborative filtering (CF) approaches. It is a universal algorithm that could reduce the cold-start problem in an earlier phase where more of the items are cold, and also in the transition phase, where the number of cold-start items decreases. Also, these algorithms could be used for diversification and to help CF approaches with the “filter bubble” problem. We prove our approach with an offline experiment in the area of movie recommendation and discuss the results.