  Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds
of thousands of players every year. Despite the large player base, it is still
important to attract new players to prevent the community of a game from
becoming inactive. Entering MOBA games is, however, often demanding, requiring
the player to learn numerous skills at once. An important factor of success is
buying the correct items which forms a complex task depending on various
in-game factors such as already purchased items, the team composition, or
available resources. A recommendation system can support players by reducing
the mental effort required to choose a suitable item, helping, e.g., newer
players or players returning to the game after a longer break, to focus on
other aspects of the game. Since Sequential Item Recommendation (SIR) has
proven to be effective in various domains (e.g. e-commerce, movie
recommendation or playlist continuation), we explore the applicability of
well-known SIR models in the context of purchase recommendations in Dota 2. To
facilitate this research, we collect, analyze and publish Dota-350k, a new
large dataset based on recent Dota 2 matches. We find that SIR models can be
employed effectively for item recommendation in Dota 2. Our results show that
models that consider the order of purchases are the most effective. In contrast
to other domains, we find RNN-based models to outperform the more recent
Transformer-based architectures on Dota-350k.
