A Comparison of Automated Journal Recommender Systems

A Compa ison o Au oma ed Jou nal Recommende Sys ems Elias En up1[0000−0002−7380−1189], Ralph Ewe h1,3[0000−0003−0918−6297], and Ane Hoppe1,3[0000−0002−1452−9509] 1TIB – Leibniz In o ma ion Cen e o Science and Technology, Hanno e , Ge many 2L3S Resea ch Cen e , Leibniz Uni e si y Hanno e , Ge many [email p o ec ed] Abs ac . Choosing he igh jou nal o an a icle can be a challenge. Au oma ed manusc ip ma ching can help au ho s wi h he decision by ecommending sui able jou nals based on use -de ined c i e ia. Se e al app oaches o e icien ma ching ha e been p oposed in he esea ch li - e a u e. Howe e , only a ew ac ual ecommende sys ems a e a ailable o end use s. In his pape , we p esen an o e iew o a ailable se ices and compa e hei key cha ac e is ics such as inpu alues, unc ionali- ies, and p i acy. We conduc a quan i a i e analysis o hei ecommen- da ion esul s: (a) examining he o e lap in he esul s and poin ing ou he simila i ies among hem; (b) e alua ing hei quali y wi h a compa - ison o hei accu acy. Due o he p o ide s’ lack o anspa ency abou he used echnologies, he esul s canno be easily in e p e ed. This high- ligh s he need o openness abou he used algo i hms and da a se s. Keywo ds: Scien i ic publishing ·Recommende sys ems. 1 In oduc ion The e e -g owing numbe o jou nals and equi emen s by unding agencies make i inc easingly di icul o esea che s o ind jou nals o hei manusc ip s. Apa om se e al publica ion guides [2,26,3,31], he au oma ed ecommenda- ion o jou nals is an ac i e ield o s udy [32,39,41,23]. An o e iew is p o ided in [1]. While ecommenda ion app oaches based on e.g. co-au ho ne wo ks [23] exis , he majo i y elies on seman ic simila i y o he use inpu o al eady pub- lished scien i ic a icles. Mos o he p oposed sys ems do no un in a p oduc ion mode a ailable o end use s. Two p io a icles compa e a ailable jou nal ecommenda ion se ices: In [13], se en se ices a e compa ed o ea u es, and illus a i e que y esul s a e p e- sen ed. The analysis includes he se ices p o ided by Cla i a e, Co ac o (since a chi ed [8]), Edanz, Else ie , IEEE, JANE, Jou nalGuide, and Sp inge . In [22] he se en ecommenda ion se ices by Edanz, Else ie , Enago, IEEE, JANE, Jou nalGuide, and Sp inge a e compa ed. The use ulness o hese se ices is analysed in compa ison o he publica ion habi s o 15 in e iewed esea che s. None o he abo e-men ioned esea ch p o ides a quan i a i e compa ison o 2 E. En up e al. Table 1: Lis o ecommende sys ems, used abb e ia ion, he p o ide , and he scope which desc ibes he subg oup o jou nals sugges ed. Recommende Name Abb e ia ion P o ide Scope Bibliome ic and Seman ic Open Access Recommende Ne wo k [4] B!SON TIB and SLUB Open Access Cha leswo h Au ho Se ices Jou nal Finde [6] Cha leswo h ASJF Cha leswo h Au ho Se ices All eCon en P o Jou nal Finde [9] eCon en P o JF eCon en P o All Edanz Jou nal Selec o [10] Edanz JS Edanz (M3) All Else ie Jou nal Finde [11] Else ie JF Else ie Publishe Food Science and Technology Abs ac s Jou nal Finde [14] FSTA JF FSTA/IFIS Food / Heal h Ins i u e o Elec ical and Elec onics Enginee s Publica ion Recommende [17] IEEE PR IEEE Publishe Jou nal / Au ho Name Es ima o [36] JANE The Bioseman ics G oup Medicine Jo [37] Jo Townsend Lab Medicine Jou nal Guide [18] Jou nal Guide Resea ch Squa e All MDPI Jou nal Finde [21] MDPI JF MDPI Publishe Resea che Jou nal Finde [24] Resea che JF Resea che App All Resea che .Li e Jou nal Finde [25] Resea che .Li e JF Resea che .Li e All Sage Jou nal Recommende [28] Sage JR Sage Publishing Publishe ScienceGa e Jou nal Finde [30] ScienceGa e JF ScienceGa e Publishe Sp inge Jou nal Sugges e [34] Sp inge JS Sp inge Na u e Publishe Taylo & F ancis Jou nal Sugges e [35] T&F JS Taylo & F ancis All T inka Jou nal Finde [38] T inka JF T inka AI All Wiley Jou nal Finde [40] Wiley JF Wiley Publishe Web o Science / EndNo e Manusc ip Ma che [7] WoS MM Cla i a e All jou nal ecommende sys ems; bo h only include a subse o he a ailable se - ices and compa e hem using examples o expe e alua ions. In his pape , we analyse he 20 cu en ly a ailable jou nal ecommende sys ems (as o June 6, 2023). We p o ide an o e iew o inpu op ions, as well as il e and sea ch ea u es. In con as o p e ious wo k, we pe o m a quan i a i e e alua ion by measu ing he accu acy and he numbe o o e lapping esul s. As a esul , we d aw conclusions abou how well he se ices pe o m and com- plemen each o he . The pape is o ganised as ollows: Sec ion 2 desc ibes he se ices selec ed o he compa ison in his pape . A ea u e compa ison wi h a desc ip ion o he scope, inpu , and il e s o he se ices ollows. The quan- i a i e analysis o o e lapping esul s and accu acy is p esen ed in Sec ion 3. Sec ion 4 summa ises ou indings and de i es implica ions o use s. A Compa ison o Au oma ed Jou nal Recommende Sys ems 3 2 Selec ion and Quali a i e Compa ison The ecommende se ices in his s udy we e ound using “jou nal ecommende ”, “manusc ip ma che ” and “jou nal inde ” as a que y o Google and Bing, and e alua ing he esul s on he i s h ee pages. This compa ison only consid- e s jou nal ecommenda ion se ices ha o e a o m o au oma ic manusc ip ma ching. I excludes se ices ha only o e o il e jou nals. We only conside se ices ha a e cu en ly online and ha wo k wi h au oma ed (no expe ) ecommenda ions. The sea ch esul ed in 20 ecommende sys ems p esen ed in Table 1. In he ollowing, we will abb e ia e hei names as indica ed. 2.1 Desc ip ion o Se ices As shown in Table 1, se en ou o 20 se ices only deli e esul s ha a e pa o he publishe p o iding he ool. O he es , one is ocused on open access and wo on medicine. The Cha leswo h ASJF, JANE, Jo , Resea che JF, and T inka.AI JF include p e-p in se e s in hei esul s. Only B!SON and Jo a e open-sou ce. B!SON, he Else ie JF, JANE, Jo , and he WoS MM ha e been desc ibed in esea ch pape s. The B!SON ecom- mende uses Elas icsea ch, a neu al ne wo k, and bibliog aphic coupling o ec- ommend jou nals [12,5]. The Cha leswo h Jou nal Finde claims ha i s sea ch is powe ed by Resea che JF. The esul s, howe e , a e di e en . The Else ie JF uses BM 25 o ind one million simila a icles and a e ages he sco es o each jou nal [19]. JANE uses Lucene’s Mo eLikeThis algo i hm o ind he 50 mos simila a icles o he use inpu [29], sums he sco es pe jou nal and no malises hem. Jo is based on JANE and adds coun ing o he jou nal appea ances in a use -p o ided lis o e e ences [15]. The WoS MM a e ages he esul s o a Suppo Vec o Machine and a Lucene k-Nea es -Neighbo s sea ch [27]. 2.2 Sea ch Inpu While a ibu es such as ull ex [16] o au ho s [20] ha e been used in esea ch o sugges jou nals, mos se ices use i le and abs ac . Keywo ds and subjec a e also used by a ew se ices. B!SON wo ks wi h e e ences by pa sing o DOIs in he ex he use en e s (copied om he PDF o a s uc u ed o ma like bib ex); Jo expec s a bibliog aphy ile in he RIS o ma . The Cha leswo h ASJF, Edanz JS, IEEE PR, JANE, and Resea che JF use a single inpu ield o se e al a ibu es a once. The ScienceGa e JF i s sugges s se e al, edi able keywo ds based on he i le and abs ac which a e hen used o he ecommenda ions. 2.3 Fil e ing, So ing and O he Fea u es Mos se ices o e il e and so ing op ions o he sco e, i le, publishe , pub- lica ion ime, open access o jou nal impac ac o . The Cha leswo h ASJF, eCon en P o JF, Resea che JF, T&F JS, and Wiley JF ha e ew o no il e , o so ing op ions. 4 E. En up e al. In he ollowing, we will lis no ewo hy ea u es o he sys ems: B!SON acili a es he sea ch wi h an al eady published a icle by e ching he inpu s ia e.g. C oss e . Else ie JF o e s o en e he au ho ’s o ganisa ion o ge pe sonalised publishing op ions based on exis ing ag eemen s. I also de ec s i he inpu da a belong o an a icle al eady published by Else ie . The IEEE PR can il e enues o publish be o e a speci ied da e and also sea ches o con e ences (no conside ed in his pape ). JANE allows sea ching o simila a icles and au ho s who published simila wo k. Jo p o ides a wo-dimensional isualisa ion wi h he “p ospec ” (es ima ed chance o accep ance) on he X- axis and an impac me ic (e.g. Ci eSco e) on he Y-axis. Jou nal Guide has a compa ison unc ion o c ea e an o e iew o selec ed jou nals om he esul lis . 2.4 T anspa ency and P i acy Only B!SON and Jo a e open sou ce, bu se e al ecommende sys ems show which simila a icles led o he ecommenda ion o a jou nal: B!SON, Edanz JS, JANE, Jo , Jou nal Guide, Resea che .Li e JF, Sage JR, and T inka.AI JF. Mos se ices do no publicise which jou nals a e in hei da a se and i i is up- o-da e. The websi es o en, a leas , indica e he numbe o jou nals included. Bo h he Jou nal Guide and JANE ha e he op ion o sc amble he en e ed abs ac on he clien side o p i acy. All sys ems o e an enc yp ed TLS con- nec ion; Jo , howe e , ea u es an expi ed ce i ica e a he ime o w i ing. The majo i y o ecommende sys ems a e ee and can be used anonymously. Howe e , he WoS MM and he T inka.Ai JF only wo k wi h an accoun . Re- sea che .Li e JF equi es an accoun o ad anced ea u es such as iewing simila a icles. Simila ly, he eCon en P o JF equi es he name and e-mail add ess o a manda o y sign-up o hei e-mail communica ions. The T&F JS explici ly s a es ha hey s o e he submi ed abs ac s and which esul s he use clicks on. The T inka.AI JF also s o es he inpu along wi h he gene a ed esul s so he use can e iew hem la e . The e is no op ion o dele e sea ches. Only B!SON and he Edanz JS p omise o no s o e he use inpu s. 3 Quan i a i e E alua ion In he ollowing, we pe o m a quan i a i e compa ison o he accu acy and he o e lap o he esul s. We used smalle a icle es se s o a oid ge ing blocked. 3.1 Compa ison o Independen Recommende Sys ems We es he publishe -independen ecommende sys ems wi h 50 a icles om he only jou nal we ound in all ecommende s: “New Bio echnologies” (ISSN 1876-4347). Simila ly o esea ch on web sea ch engine esul s [33], we p esen he a e age o e lap o he op 15 esul s based on he ISSNs in Table 2. A Compa ison o Au oma ed Jou nal Recommende Sys ems 5 Table 2: Compa ing he a e age o e lap o esul s o he publishe -independen ecommende s sys ems B!SON Cha leswo h ASJF eCon en P o JF Edanz JS FSTA JF JANE Jo Jou nal Guide Resea che JF Resea che .Li e JF ScienceGa e JF T inka.AI JF WoS MM B!SON 15.0 1.5 0.0 1.3 0.5 2.6 2.9 2.0 2.9 2.1 1.9 4.5 2.0 Cha leswo h ASJF 1.5 7.0 0.0 1.3 0.6 2.7 3.6 2.8 5.6 1.5 1.7 2.8 2.3 eCon en P o JF 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Edanz JF 1.3 1.3 0.0 15.0 0.4 3.6 3.6 1.5 2.7 2.1 2.6 4.2 3.6 FSTA JF 0.5 0.6 0.0 0.4 4.6 0.6 0.6 0.4 0.8 0.6 0.8 0.8 0.6 JANE 2.6 2.7 0.0 3.6 0.6 15.0 8.9 3.3 3.7 2.6 2.3 4.4 3.5 Jo 2.9 3.6 0.0 3.6 0.6 8.9 14.7 4.1 5.0 2.6 2.4 5.1 3.7 Jou nal Guide 2.0 2.8 0.0 1.5 0.4 3.3 4.1 14.4 3.4 1.8 1.5 2.9 1.9 Resea che JF 2.9 5.6 0.0 2.7 0.8 3.7 5.0 3.4 15.0 2.4 2.9 4.9 4.3 Resea ch.Li e JF 2.1 1.5 0.0 2.1 0.6 2.6 2.6 1.8 2.4 14.4 2.4 3.9 2.7 ScienceGa e JF 1.9 1.7 0.0 2.6 0.8 2.3 2.4 1.5 2.9 2.4 15.0 4.2 3.5 T inka.AI JF 4.5 2.8 0.0 4.2 0.8 4.4 5.1 2.9 4.9 3.9 4.2 14.6 4.5 WoS MM 2.0 2.3 0.0 3.6 0.6 3.5 3.7 1.9 4.3 2.7 3.5 4.5 15.0 The Cha leswo h ASJF, eCon en P o JF, and FSTA JF p o ide ewe e- sul s, he es o he se ices usually p o ide he 15 esul s ha we e consid- e ed. Some que ies did no e u n any o only ew esul s. The p ominen o e - lap be ween Cha leswo h ASJF and Resea che JF con i ms ha Cha leswo h ASJF’s ecommenda ions a e based on Resea che JF (see Sec ion 2.1). A simila e ec can be obse ed wi h JANE and Jo . The eCon en P o JF and FSTA JF sha e he leas esul s wi h he o he sys ems. A leas o FSTA JF, his migh be caused by i s e y speci ic scope. The o he sys ems usually sha e wo o ou esul s, wi h T ink.AI JF showing he highes o e laps wi h o he se ices. 3.2 Accu acy We u he es he accu acy (p ecision) o he ecommende sys ems. To ensu e a ai compa ison, we es wi h a icles om jou nals in hei da a se (i.e. es he Else ie JF only wi h Else ie a icles). Each sys em is es ed on 100 a icles, coming om 100 di e en jou nals o b oaden he scope o es ing. A icles om his yea a e excluded so ha we can assume ha he a icle should be in he aining se . As mos sys ems do no disclose he included jou nals, we used es que ies o iden i y a lis o jou nals in hei da a se . We use he API o he scien i ic da abase Dimensions3 o e ie e he co esponding es a icles. We also assume ha he co ec jou nal is he one whe e he a icle was published. 3h ps://docs.dimensions.ai/dsl/ 6 E. En up e al. Table 3: Recommende sys ems and hei accu acy conside ing he i s and he i s en esul s. Name Acc@1 Acc@10 B!SON 0.20 0.88 Cha leswo h ASJF 0.21 0.77 eCon en P o JF 0.03 0.16 Edanz JS 0.16 0.54 Else ie JF 0.35 0.86 FSTA JF 0.07 0.29 IEEE PR 0.26 0.68 JANE 0.83 0.96 Jo 0.19 0.93 Jou nal Guide 0.38 0.98 MDPI JF 0.48 0.88 Resea che JF 0.07 0.49 Resea che .Li e JF 0.15 0.48 Sage JR 0.17 0.69 Sp inge JS 0.97 0.98 T&F JS 0.48 0.91 T inka.AI JF 0.07 0.41 ScienceGa e JF 0.10 0.35 Wiley JF 0.19 0.59 WoS MM 0.12 0.48 The sys ems migh ake o he ac- o s in o accoun apa om he se- man ic ma ch, e.g. possible impac . Ha ing he es a icles po en ially in he aining se is a limi a ion. Ne - e heless, high accu acy can indica e how much he sys em elies on inding a simila a icle. The esul s a e shown in Table 3. As JANE is checking o simila a - icles [29], he accu acy is high be- cause i will usually ind he a icle in ques ion in i s da a se . Jou nal Guide and Sp inge JS also yield high accu- acy. The eason o eCon en P o JF’s, FSTA JF’s, and ScienceGa e JF’s low accu acies a e unclea . 4 Conclusions In his pape , we sys ema ically com- pa ed 20 jou nal ecommenda ion se - ices. We ound ha mos o hem use he i le and abs ac o ind he bes - i ing jou nal. Apa om publishe - speci ic se ices, 13 independen se - ices exis . Many y o in o m he use how a ma ch was calcula ed, bu ew ha e published hei sou ce code, ecommenda ion app oach, o da a sou ces. We es ed he accu acy o he se ices and o wha deg ee hey deli e ed he same esul s. The accu acy a ies widely wi h he Acc@10 anging om 16% o 98%. While o mos ecommende sys ems wo o ou esul s a e sha ed, a highe o e lap alida es he sha ed ecommenda ion app oach o some se ices. We de i e he ollowing ad ice: (a) Use s should look beyond he i s sug- ges ion. (b) Fo he medical domain, Jo p o ides mo e ea u es han JANE and can be ecommended. (c) Fo open-access publica ions, B!SON and Jou nal Guide can be ecommended. B!SON is mo e anspa en bu bo h se ices ha e a high accu acy and numbe o so ing and il e op ions. (d) O he wise, Jou nal Guide o publishe -speci ic se ices can be used. Backg ound knowledge is s ill equi ed o he inal decision. Decla a ion o Compe ing In e es s The au ho s we e pa o he B!SON p ojec . A Compa ison o Au oma ed Jou nal Recommende Sys ems 7 Re e ences 1. Ajmal, S., Muzammil, M.B.: PVRS: Publica ion Venue Recommenda ion Sys- em A Sys ema ic Li e a u e Re iew. 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