Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on longterm user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple “real-time” recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user’s long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and shortterm adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for ...

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Dietmar Jannach, Lukas Lerche, Michael Jugovac
• Keywords: Control Systems, RECSYS 2015