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 |s2cid=13907106 }}</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. Systematic analysis of publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW, RecSys, IJCAI), has shown that on average less than 40% of articles are reproducible, with as little as 14% in some conferences. Overall the studies identify 26 articles, only 12 of them could be reproduced and 11 of them could be outperformed by much older and simpler properly tuned baselines. The articles also highlights a number of potential problems in today's research scholarship and call for improved scientific practices in that area. <ref>{{cite journal |last1=Ferrari DacremaRendle |first1=MaurizioSteffen |last2=BoglioKrichene |first2=SimoneWalid |last3=CremonesiZhang |first3=PaoloLi |last4=JannachAnderson |first4=DietmarJohn |title=ANeural TroublingCollaborative AnalysisFiltering ofvs. ReproducibilityMatrix andFactorization Progress in Recommender Systems ResearchRevisited |journal=Fourteenth ACM TransactionsConference on InformationRecommender Systems |date=822 JanuarySeptember 2021 |volume=39 |issue=22020 |pages=1–49240–248 |doi=10.1145/3434185 |url=https://dl.acm.org/doi/10.1145/3434185 |arxiv=19113383313.076983412488}}</ref> Similar issues have been spotted also in sequence-aware recommender systems.<ref>{{cite journal |last1=Ferrari DacremaLudewig |first1=MaurizioMalte |last2=CremonesiMauro |first2=PaoloNoemi |last3=JannachLatifi |first3=Sara |last4=Jannach |first4=Dietmar |title=ArePerformance WeComparison Reallyof MakingNeural Muchand Progress?Non-neural AApproaches Worryingto Analysis of Recent NeuralSession-based Recommendation Approaches |journal=Proceedings of the 13th ACM Conference on Recommender Systems |date=2019 |pages=101–109462–466 |doi=10.1145/3298689.33470583347041 |url=https://dl.acm.org/authorizecitation.cfm?N684126id=3347041 |accessdate=16 October 2019 |publisher=ACM|isbn=9781450362436 |doi-access=free }}</ref>
|accessdate=16 October 2019 |publisher=ACM|hdl=11311/1108996 |arxiv=1907.06902 |isbn=9781450362436 |s2cid=196831663 }}</ref><ref>{{cite journal |last1=Rendle |first1=Steffen |last2=Krichene |first2=Walid |last3=Zhang |first3=Li |last4=Anderson |first4=John |title=Neural Collaborative Filtering vs. Matrix Factorization Revisited |journal=Fourteenth ACM Conference on Recommender Systems |date=22 September 2020 |pages=240–248 |doi=10.1145/3383313.3412488}}</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>
