Abstract
Recommender Systems are a popular and common means to extract relevant information for users. Small and medium-sized enterprises make up a large share of the overall amount of business but need to be more frequently considered regarding the demand for recommender systems. Different conditions, such as the small amount of data, lower computational capabilities, and users frequently not possessing an account, require a different and potentially a more small-scale recommender system. The requirements regarding quality are similar: High accuracy and high diversity are certainly an advantage. We provide multiple solutions with different variants solely based on information contained in event-based sequences and temporal information. Our code is available at GitHub (https://github.com/lmu-dbs/DP-Recs). We conduct experiments on four different datasets with an increasing set of items to show a possible range for scalability. The promising results show the applicability of these grammar-based recommender system variants and leave the final decision on which recommender to choose to the user and its ultimate goals.
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Zellner, L., Rauch, S., Sontheim, J., Seidl, T. (2024). On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_10
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DOI: https://doi.org/10.1007/978-981-97-2262-4_10
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