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In recent years a number of neural and deep-learning techniques have been proposed, some of which generalize traditional [[Matrix factorization (recommender systems)|Matrix factorization]] algorithms via a non-linear neural architecture <ref>{{cite journal |last1=He |first1=Xiangnan |last2=Liao |first2=Lizi |last3=Zhang |first3=Hanwang |last4=Nie |first4=Liqiang |last5=Hu |first5=Xia |last6=Chua |first6=Tat-Seng |title=Neural Collaborative Filtering |journal=Proceedings of the 26th International Conference on World Wide Web |date=2017 |pages=173–182 |doi=10.1145/3038912.3052569 |url=https://dl.acm.org/citation.cfm?id=3052569 |accessdate=16 October 2019 |publisher=International World Wide Web Conferences Steering Committee|isbn=9781450349130 |arxiv=1708.05031 }}</ref>.
While deep learning has been applied to many different scenarios: context-aware, sequence-aware, social tagging etc. its real effectiveness when used in a simple [[Collaborative filtering]] scenario has been put into question. A systematic analysis of publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW, RecSys), has shown that on average less than 40% of articles are reproducible, with as little as 14% in some conferences. Overall the study identifies 18 articles, only 7 of them could be reproduced and 6 of them could be outperformed by much older and simpler properly tuned baselines. The article also highlights a number of potential problems in today's research scholarship and calls for improved scientific practices in that area.<ref>{{cite journal |last1=Ferrari Dacrema |first1=Maurizio |last2=Cremonesi |first2=Paolo |last3=Jannach |first3=Dietmar |title=Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches |journal=Proceedings of the 13th ACM Conference on Recommender Systems |date=2019 |pages=101–109 |doi=10.1145/3298689.3347058 |url=https://dl.acm.org/authorize?N684126
|accessdate=16 October 2019 |publisher=ACM|hdl=11311/1108996 |arxiv=1907.06902 }}</ref> Similar issues have been spotted also in sequence-aware recommender systems.<ref>{{cite journal |last1=Ludewig |first1=Malte |last2=Mauro |first2=Noemi |last3=Latifi |first3=Sara |last4=Jannach |first4=Dietmar |title=Performance Comparison of Neural and Non-neural Approaches to Session-based Recommendation |journal=Proceedings of the 13th ACM Conference on Recommender Systems |date=2019 |pages=462–466 |doi=10.1145/3298689.3347041 |url=https://dl.acm.org/citation.cfm?id=3347041 |accessdate=16 October 2019 |publisher=ACM|isbn=9781450362436 |doi-access=free }}</ref>
==See also==
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