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“What are/is the state-of-the-art recommendation algorithm(s)?” is a question that should be a no-brainer to answer for any recommender-system researcher and developer. However, the answer is typical “I don’t know, at least not for sure”.
“6 of [the algorithms could] often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. The remaining one clearly outperformed the baselines but did not consistently outperform a well-tuned nonneural linear ranking method.“
We tested five recommendation algorithms on six German news websites. On almost every website a different algorithm was best (and worst). ‘User-based CF’ performed best on ksta.de, the ‘Most popular sequence’ performed best on sport1.de, the normal most ‘popular algorithm’ performed best on ciao.de, and content-based filtering performed best on motor-talk.de.
Even worse, algorithms performed differently at different times, for different genders etc. For instance, between 18:00 and 4:00 o’clock, the most popular sequence algorithm performed best (see below). Between 4:01 and 17:59 o’clock, the standard “most popular” algorithm performed best.