Utilizing Hybrid Information Sources to Learn Representations of Cards in Collectible Card Video Games

We investigate the problem of learning representations of cards in collectible card video games. Our goal is to utilize such representations in modeling contextual similarity between cards. When constructed appropriately, such similarity models can offer many benefits to players. In particular, one can employ them to recommend cheaper or more available card replacements in popular decks. To this end, we utilize some known NLP methods, such as word2vec and Latent Semantic Analysis, to extract card embeddings from their base characteristics and textual descriptions. We also propose two new approaches that make use of information regarding multiple decks constructed by the community of players and attempt to capture the notion of card interchangeability. We empirically validate the described methods and compare their performance using data obtained for two popular games, Hearthstone: Heroes of Warcraft and Clash Royale. In the experiments, we consider various representations of cards and then, we derive the corresponding similarities. To validate the compared methods, we check how consistent are the similarity measurements, which they produce with the assessments made by experienced players. Results of our analysis show that combining outcomes of methods that work with different sources of information, i.e., textual descriptions of individual cards and deck-specific card co-occurrences, can improve performance in the task of similarity assessment. Moreover, a clustering of cards in the constructed vector space can provide some interesting insights for the community of players. As already mentioned, it can be used to suggest replacements of cards that players lack in their collections or to indicate cards that are likely to deteriorate win chances of particular decks.