=Paper=
{{Paper
|id=Vol-3178/CIRCLE_2022_paper_17
|storemode=property
|title=Fusion strategies to combine topical and temporal information for publication venue recommendation
|pdfUrl=https://ceur-ws.org/Vol-3178/CIRCLE_2022_paper_17.pdf
|volume=Vol-3178
|authors=Luis M. de Campos,Juan M. Fernández-Luna,Juan F. Huete
|dblpUrl=https://dblp.org/rec/conf/circle/CamposFH22
}}
==Fusion strategies to combine topical and temporal information for publication venue recommendation==
https://ceur-ws.org/Vol-3178/CIRCLE_2022_paper_17.pdf
Fusion strategies to combine topical and temporal
information for publication venue recommendation
Luis M. de Campos, Juan M. Fernández-Luna and Juan F. Huete
Departamento de Ciencias de la Computación e Inteligencia Artificial, ETSI Informática y de Telecomunicación,
CITIC-UGR, Universidad de Granada, 18071 Granada, Spain
Abstract
We study the publication venue recommendation problem, where a recommender system must help
a researcher to decide where to submit a given target article for possible publication. We focus on
content-based recommendation approaches, where we explicitly look for a good match between the
topics discussed in the article and the recommended venue. But, in addition to this topical dimension,
we also want to include a temporal dimension, in such a way that we prefer those venues where the
articles which are more related with the target article have been published more recently. We use
an information retrieval system to obtain separate topical and temporal recommendations and then
combine them by means of different fusion strategies. The experimental results obtained on a collection
of biomedical journal articles confirm the effectiveness of our proposals.
Keywords
Publication venue recommendation, topical profiles, temporal information, decay functions, informa-
tion retrieval, content-based recommendation, fusion strategies
1. Introduction
Every researcher in any discipline has to face the problem of deciding where to publish the
scientific article he/she has written. This decision, among other factors, should be based on
the suitability of the topics that the article deals with respect to those usually treated by the
tentative publication venues (either journals or conferences). This is known as the publication
venue recommendation problem [28, 45].
Although there are also approaches to this problem based on collaborative filtering (rec-
ommending venues where either similar researchers or coauthors have published in the past
[25, 31]), in this paper we are going to adopt a content-based approach, where the decision
about which venue to recommend is mainly based on the textual content of the paper to be
published [20, 42].
The information that the recommender system possesses about each possible publication
venue can be represented by means of a profile [19]. Although there are different types of
profiles, for example based on semantic networks or concepts [39], the most common and
CIRCLE’22: Joint Conference of the Information Retrieval Communities in Europe, July 4–7, 2022, Samatan, France
lci@decsai.ugr.es (L. M. de Campos); jmfluna@decsai.ugr.es (J. M. Fernández-Luna); jhg@decsai.ugr.es
(J. F. Huete)
0000-0001-9125-1195 (L. M. de Campos); 0000-0002-0366-4545 (J. M. Fernández-Luna); 0000-0002-9970-7705
(J. F. Huete)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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CEUR Workshop Proceedings (CEUR-WS.org)
simple type that can be associated to an item in general, or to a venue in our particular scenario,
is based on a set of terms or keywords (perhaps weighted) [16]; for example, a textual description
of the item, or a subset of the terms that appear in the articles published in the venue. These
term-based profiles can be built automatically.
In [10] we proposed the use of several homogeneous subprofiles to represent each venue,
as an alternative to a single and heterogeneous profile comprising all the information about
it. The intuition is that if we are able to describe with more precision which are the main
topics covered by each venue (these topics being obtained analyzing the articles published in
it through a clustering process) and we build separate subprofiles for each topic, probably we
could recommend more accurately the most appropriate venues for publishing a new article.
However, we did not consider other information source that could also help to improve the
recommendation results, namely the temporal information. The motivation for using this source
for publication venue recommendation is that we do not only want that the thematic content of
our new (target) article matches with the recommended venues, we would also prefer that the
themes this article deals with have been recently considered in these venues: If venues A and
B both have published articles related with the main topic of the target article but the articles
in A are more recent than the articles in B, then probably we would prefer to select venue A
instead of B.
In this work we aim to build a publication venue recommendation system considering both
the temporal and the topical dimension of the available data (the articles published in the
venues). Therefore, on the one hand, we want to study how the temporal information alone can
be used in the publication venue recommendation problem and the benefits it may offer; on the
other hand, we also want to study different ways of combining the temporal information with
the topical information, hoping that these two complementary sources reinforce each other.
The remainder of this paper is organized as follows: Section 2 briefly discusses some related
works. Section 3 describes how we build the topical subprofiles by means of Latent Dirichlet
Allocation (LDA) [6] applied to the articles published in a set of venues, and how to use them
within an information retrieval system (IRS). Section 4 explains how the temporal information
of the articles, through the use of a decay function, can be incorporated to modify the output
of another IRS for articles. Then, in Section 5 the way of building the publication venue
recommender from either the IRS of subprofiles or the IRS of articles is outlined. Section 6 is
devoted to study methods to combine the topical and temporal recommenders. Section 7 reports
the results of the experiments with our models and the baselines. Finally, Section 8 contains the
concluding remarks.
2. Related work
In the literature about content-based publication venue recommendation there are different
ways of representing the information within the profiles (terms, n-grams, noun-phrases, topics)
but in all the cases each venue has either a single profile [28, 40, 45] or as many subprofiles as
published articles [20, 35, 25, 45, 32, 37, 17]. The only work where different subprofiles topically
homogeneous are built for each venue is [10].
Moreover, there are not much works explicitly using temporal information: [2] uses a personal
venue rating which takes into account the years when the articles published in a venue were
added to the researcher personal collection. This rating is used to compute a similarity between
researchers which is then applied within a collaborative filtering algorithm to recommend
venues. In [32], within their system, the authors compute a similarity between venues that
gives more weight to the information of the articles associated to more recent years using an
age-discounted scheme (inverse log-weighting scheme), and this similarity is then used to build
a graph of venues where a random walk with restart algorithm is applied.
The situation is different in the context of general recommender systems, where there are
many works which consider organizations of the information different from single profiles.
These multi-faceted profiles can be trees [30], graphs of clusters [47] or hierarchies [21, 36]. Other
methods generate topical subprofiles using clustering (grouping either terms or documents)
[3, 5, 11, 13, 26].
Within general recommender systems there are also many works considering the inclusion of
temporal information, although most of them are focused on collaborative filtering techniques
[8]. A quite common method is to use decay functions or temporal discounting to penalize old
items [15, 46]. Another option is to consider time frames. For example, [33] studies a content-
based filter for tweets, learning a specific time frame for each user and only recommending
tweets within this personalized frame. Similar ideas are used in [38] for points-of-interest recom-
mendation. Long and short-term profiles are another way to incorporate time, by distinguishing
the most recent interests of users from those more stable or permanent [23, 44].
3. Topical subprofiles
Two basic organizational schemes to represent information about the different publication
venues are that we call monolithic profiles and atomic subprofiles, which represent the two
extremes in terms of heterogeneity or homogeneity of the information. In monolithic profiles
all the articles published in a given venue are grouped together in a single (heterogeneous)
macro-document. On the contrary, in atomic subprofiles each individual article published in
a venue forms a (very homogeneous) subprofile for this venue, having as many subprofiles
as articles it contains. An intermediate way of organizing the information would be to build
subprofiles around the different thematic areas covered by each venue.
In [10] we used clustering (namely k-means) to group the published articles in homogeneous
groups based on their textual content. Then the text of all the articles in a venue which belong
to the same group was used to build the corresponding subprofile for this venue (one subprofile
for each cluster). In this paper we are going to build topical profiles in a similar way but using
LDA instead of clustering.
We propose to apply a global LDA to the collection of all the articles of all the venues1 . It is
known that LDA tries to find latent topics in a document collection. Each of these latent topics
is characterized by a different probability distribution of terms (given the topic). Moreover, in
this non-supervised method, for each document a probability distribution of topics is obtained,
which describes to which extent each document is about each topic. LDA requires the number
of topics to be used, 𝑘, as an input parameter.
1
In contraposition to use a separate, local LDA applied to all the articles of each venue.
Once LDA has been applied to our articles collection and we have computed the probability
distributions of topics, we associate each article with its most probable topic. Therefore, we
have a partition of the articles in 𝑘 groups, where all the articles in each group deal mainly
about the same global topic.
The next step is to obtain the homogeneous subprofiles for each publication venue. We simply
divide each group of articles associated with the same topic into local groups of articles related
to this topic and each venue. All the articles in each local group associated with a venue are
then concatenated to form a single document/subprofile. In this way each venue will have at
most 𝑘 subprofiles, probably much less than 𝑘, because it is quite possible that the vast majority
of the venues do not deal with all the topics but are especialized in a more reduced number of
specific topics (and therefore there will be topics which are not the most probable topic for any
article in a given venue).
Finally, the document collection formed by the subprofiles of all the venues is indexed by an
IRS that will be used to match the content of the target article (the query) with the subprofiles,
providing a score, 𝑠𝑐𝑠 (𝑝), for each subprofile 𝑝 and hence a ranking of subprofiles.
4. Managing temporal information
We assume that a researcher normally will prefer to publish in a venue where the topics related
to their article are dealt with recently, i.e. they are topics of current interest of the venue. In
other words, a venue that has recently published articles which are closer to the target article is
preferable. Although reasonable and intuitive, this is only a working hypothesis, which our
experiments in Section 7 will confirm2 .
A simple but effective way of incorporating this criterion in the recommendation is through
the use of a temporal decay function 𝑓 (∆𝑡) [22]. This function considers the difference, ∆𝑡,
between the current year3 and the publication year of an already published article, in such a
way that the greater ∆𝑡 is the less important becomes this article (from a temporal perspective).
Then, the recommender system based on temporal information will work in the following
way: we index the collection of articles (the atomic collection) using an IRS. Given the target
article, the IRS obtains a score 𝑠𝑐𝑎 (𝑑) for each article 𝑑 in the collection, which represents the
degree of similarity between the target and 𝑑. Then we compute a modified score 𝑚𝑠𝑐𝑎 (𝑑) by
using also the decay function, 𝑚𝑠𝑐𝑎 (𝑑) = 𝑠𝑐𝑎 (𝑑) × 𝑓 (∆𝑡), which reduces the similarity of the
article as the temporal distance between the target and 𝑑 increases. Finally, we obtain a ranking
of articles according to this modified score.
5. Building the publication venue recommenders
In the previous models, the ranking obtained by the IRS, given the target article as the query, is
either a ranking of individual articles (for the temporal and the atomic models) or a ranking of
2
However, in these experiments, we only have access to the venues where the articles were in fact published but
we do not know which venues the authors considered in the first place.
3
In our context this is the year of publication of the test article.
subprofiles (for the topical model)4 . However, a ranking of publication venues is required, as
we are going to recommend venues to publish the target article. To combine the scores of the
different documents associated with each venue 𝑣 (i.e. for those articles 𝑑 ∈ 𝑣 published in 𝑣
and for those subprofiles 𝑝 ∈ 𝑣 associated to 𝑣) and generate a final venue ranking, we use the
CombLgDCS method [9], which sums up the scores of all of the subprofiles/articles associated
with each venue but taking into account their positions in the ranking (using a logarithmic
devaluation):
∑︁ 𝑠𝑐𝑠 (𝑝)
𝑠𝑐𝑡𝑜𝑝 (𝑣) = , (1)
𝑝∈𝑣
log2 (𝑟𝑎𝑛𝑘(𝑝) + 1)
∑︁ 𝑚𝑠𝑐𝑎 (𝑑)
𝑠𝑐𝑡𝑒𝑚𝑝 (𝑣) = , (2)
log2 (𝑟𝑎𝑛𝑘(𝑑) + 1)
𝑑∈𝑣
where 𝑠𝑐𝑠 (𝑝) and 𝑚𝑠𝑐𝑎 (𝑑) denote the original score values obtained by the IR systems based
on subprofiles and articles, respectively, and 𝑟𝑎𝑛𝑘(𝑝) (respect. 𝑟𝑎𝑛𝑘(𝑑)) is defined as 1 in the
case that 𝑝 (respect. 𝑑) being the first occurrence of a subprofile (respect. an article) of 𝑣 in the
original ranking and, otherwise, it is the raw value of the position of the subprofile (respect. the
article) in this ranking.
6. Combining topical and temporal information
Once we have developed publication venue recommenders based on both topical and temporal
information, the next logical step is to combine them to try to improve performance. As the
previous methods are based on IR systems and each generates a ranking of venues given the
target article, it seems reasonable to use some fusion/aggregation methods which are usually
employed to combine the rankings of different IR systems [43]. There are two basic types of
fusion methods: score-based and rank-based methods [34].
Score-based methods combine the scores generated by each IRS for a document, thus obtaining
a combined score that is used to rerank the documents. However, as we are combining the scores
of different IRSs with different characteristics (in our case scores obtained from different types
of documents, subprofiles in one case and articles in other case), previous to the combination a
score normalization is necessary to make the scores comparable to each other. We normalize the
scores of each IRS by dividing by the corresponding maximum score. Rank-based methods do
not use the scores (in case these scores exist) but only the information provided by the rankings.
Among the score-based methods, we are going to use the so-called CombSUM and CombMNZ,
proposed originally in [18], and LC, a linear combination of scores.
CombSUM simply computes the sum of the scores of the different IRSs, in our case the
combined score of venue 𝑣 is
𝑠𝑐(𝑣) = 𝑛𝑠𝑐𝑡𝑜𝑝 (𝑣) + 𝑛𝑠𝑐𝑡𝑒𝑚𝑝 (𝑣),
4
For the monolithic model we directly obtain a ranking of venues.
where 𝑛𝑠𝑐𝑡𝑜𝑝 and 𝑛𝑠𝑐𝑡𝑒𝑚𝑝 are the normalized scores generated by the topical and the temporal
recommenders, respectively (and assuming that if a venue 𝑣 does not appear in some ranking,
its corresponding score is equal to 0).
CombMNZ is similar to CombSUM but tries to promote those venues appearing more fre-
quently in the rankings, by multiplying the sum of scores by the number of rankings where the
document appears. In our case the score of venue 𝑣 is
⎨ 2 * (𝑛𝑠𝑐𝑡𝑜𝑝 (𝑣) + 𝑛𝑠𝑐𝑡𝑒𝑚𝑝 (𝑣)) if 𝑣 appears in the two rankings
⎧
𝑠𝑐(𝑣) = 𝑛𝑠𝑐𝑡𝑜𝑝 (𝑣) if 𝑣 appears only in the topical ranking
𝑛𝑠𝑐𝑡𝑒𝑚𝑝 (𝑣) if 𝑣 appears only in the temp ranking
⎩
The linear combination LC is
𝑠𝑐(𝑣) = 𝑎 * 𝑛𝑠𝑐𝑡𝑜𝑝 (𝑣) + (1 − 𝑎) * 𝑛𝑠𝑐𝑡𝑒𝑚𝑝 (𝑣),
where 𝑎 is a parameter controlling the relative importance given to the topical recommender.
Among the rank-based fusion methods, we shall use Borda Count [4, 24] and Condorcet [29],
which originally were election methods.
Borda methods transform the rankings into scores which are later combined using different
functions. Each document is associated with its position in the ranking: the first document gets
score 1, the second document obtains score 2, and so on until the last document in the ranking,
which gets score 𝑌 (if there are 𝑌 documents in the ranking). If a document does not appear in
the ranking (but we are going to consider it because it appears in another ranking), its score is
𝑌 + 1. Then the scores obtained from each ranking are combined with some functions and a
new ranking is generated (in this case sorting from least to greatest). In our case, having only
two rankings and hence two scores, 𝑠𝑐𝑏𝑡𝑜𝑝 and 𝑠𝑐𝑏𝑡𝑒𝑚𝑝 , the different functions used give rise to
the following expressions:
BordaSUM: 𝑠𝑐𝑏 (𝑣) = 𝑠𝑐𝑏𝑡𝑜𝑝 (𝑣) + 𝑠𝑐𝑏𝑡𝑒𝑚𝑝 (𝑣)
BordaPROD: 𝑠𝑐𝑏 (𝑣) = 𝑠𝑐𝑏𝑡𝑜𝑝 (𝑣) * 𝑠𝑐𝑏𝑡𝑒𝑚𝑝 (𝑣)
BordaL2: 𝑠𝑐𝑏 (𝑣) = 𝑠𝑐𝑏𝑡𝑜𝑝 (𝑣)2 + 𝑠𝑐𝑏𝑡𝑒𝑚𝑝 (𝑣)2
Another common option is to associate with each document some transformation of the
ranking instead of the ranking itself. For example, we can link with a document in position 𝑖
the score 1/𝑖, thus obtaining (using the same functions to combine the scores as before) the
Reciprocal Borda methods, BordaRSUM, BordaRPROD and BordaRL2.
The Condorcet method is based on pairwise comparisons between the documents in the
ranking: given a ranking, if document 𝑑𝑖 is previous to (is prefered to) document 𝑑𝑗 in the
ranking then we add 1 to the element 𝑚𝑖𝑗 in a matrix. This process is repeated for all the
rankings, thus obtaining at the end in 𝑚𝑖𝑗 the number of times that 𝑑𝑖 was prefered to 𝑑𝑗 . If
this number is strictly greater than half the number of existing rankings (one in our case), we
count a victory of 𝑑𝑖 on 𝑑𝑗 . In this way we are accumulating the number of victories of 𝑑𝑖 over
the other documents. This number is the score associated to 𝑑𝑖 and based on these scores a new
ranking is generated5 .
5
Ties are resolved in favor of the document having greater 𝑘 𝑚𝑖𝑘 .
∑︀
7. Experiments
In this section we are going to experimentally tests our proposals for publication venue recom-
mendation. We explain first the experimental setting and next the obtained results.
7.1. Experimental settings
We used in the experimentation a set of 309,551 biomedical journal papers extracted from the
PMSC-UGR test collection [1], namely those articles published between 2007 and 2016 in one
of the 1002 journals having more than 100 papers in this period. The information used from
each article was title, abstract, keywords and year of publication. The 276,679 articles published
between 2007 and 2015 were used as training set and the test set is formed by the 33,872 articles
published in 2016.
As mentioned previously, firstly we used LDA to find the latent topics in the training set and
secondly to build the subprofiles associated to each journal. More precisely, we employed the
implementation of LDA in the Gensim Python library, with default parameters. Before applying
LDA, in order to reduce dimensionality, we performed stopwords removal and stemming; also,
the terms appearing in more than 90% of the articles and those appearing in fewer than 750
articles were removed6 . Concerning the LDA parameter fixing the number of topics, 𝑘, we
proposed a value equal to the number of descriptors or categories in the second level of the
MeSH thesaurus, which is 110.
Concerning the decay function used by the temporal model, we have considered two options:
a linear decay, 𝑓 (∆𝑡) = 1+Δ𝑡 1
, and a power decay, 𝑓 (∆𝑡) = (1+Δ𝑡) 1
1/4 . In the first case, the
linear decay imposes a penalization more severe to older articles, whereas the power decay
penalizes them more smoothly.
All the recommendation models being considered are supported by an IRS. As the original
individual articles and also the different subprofiles are text documents (in the case of subprofiles
these documents are formed by the concatenation of the articles within each subprofile), they
form a training document collection which can be indexed and searched for. We have used
the Lucene library7 for these purposes, removing stopwords and performing stemming before
indexing and using the Language Model (with Jelinek-Mercer smoothing) as retrieval model to
compute the ranking of documents.
Each article from the test set is considered as the article for which we are seeking a rec-
ommendation (i.e. the target article), and its textual content is used to form a query to the
IRS.
In order to evaluate the quality of the recommendations offered by the different models, we
adopt a conservative but objective approach: only one journal is relevant for each query (test
article) and this is the journal where the paper has actually been published. As evaluation
measures we use accuracy@X (with X=1,5) and mean reciprocal rank, MRR. Accuracy@X
computes the ratio between the number of recommendations where the true journal at which a
test article was published is among the first 𝑋 recommended journals and the number of all the
6
All these reductions of terms are only used to apply LDA and determine the topics; the subprofiles obtained from
these topics will contain all the terms appearing in the original documents associated with the subprofile.
7
https://lucene.apache.org/
recommendations. MRR tries to reflect how high in the ranking the only relevant journal is
recommended: it is computed as the average of the inverse of the positions in the ranking at
which the actual journal where each test paper was published is found (0 if the actual venue
does not appear in the top 40 positions in the ranking).
7.2. Results
We have experimented with all the fusion methods explained in Section 6, as well as the original
Topical and Temporal models and also the baselines Monolithic and Atomic models. The results
of our experiments with the different models are displayed in Table 1 and in Figures 1, 2 and 3.
The decay function used for the temporal model (and hence for all the fusion models) in these
results is always the power decay. We do not show the results obtained when using the linear
decay because they are quite poor, even worse than the baselines. This implies that a smooth
penalization of older articles is positive but it becomes self-defeating when the penalization is
abrupt.
Table 1
Results of the experiments (best results in bold).
Model acc@1 acc@5 MRR
CombSUM 0.2408 0.5533 0.3831
CombMNZ 0.2408 0.5534 0.3830
LC(0.75,0.25) 0.2385 0.5517 0.3814
BordaPROD 0.2385 0.5516 0.3812
BordaSUM 0.2384 0.5503 0.3805
BordaL2 0.2381 0.5490 0.3797
LC(0.25,0.75) 0.2379 0.5503 0.3798
BordaRSUM 0.2354 0.5527 0.3799
BordaRPROD 0.2354 0.5519 0.3798
BordaRL2 0.2354 0.5516 0.3798
Condorcet 0.2349 0.5498 0.3782
Topical 0.2346 0.5448 0.3763
Temporal 0.2331 0.5403 0.3731
Atomic 0.2282 0.5370 0.3696
Monolithic 0.2236 0.5278 0.3653
Although the differences between the different models are not quantitatively very big, the
tendencies are clear and the three metrics being considered essentially move in the same
direction (the rankings of the systems for all of them are very similar). All the fusion strategies
improve the results of the two individual components, Topical and Temporal, which in turn
both improve the results of the two baselines, Atomic and Monolithic, which do use neither
topical nor temporal information. This confirms two facts: first, the use of topics to build
more homogeneous subprofiles, as well as the use of temporal information (in the form of
a decay function) is positive to get better venue recommendations; second, the topical and
Figure 1: Accuracy@1 of the different models.
Figure 2: Accuracy@5 of the different models.
temporal information are complementary and their combination through a fusion method
further improves the results. Moreover, the individual results for topical and temporal models
are quite similar, although topics performs slightly better than temporal.
It can also be observed that the fusion methods based on scores (CombSUM, CombMNZ and
LinearComb) perform better than those ones based only on the ranking information (Borda and
Condorcet). This confirms findings obtained in other contexts [4].
There is almost no difference between the different methods based on scores, although the
linear combination performs somewhat worse if the weights are not uniform (considering the
Figure 3: Mean reciprocal rank of the different models.
three weight combinations being used, taking into account that CombSUM is equivalent to
LinearComb with both weights equal to 0.5). In other words, we should attribute the same
importance to the topical and the temporal information (although perhaps it would be preferable
to give somewhat more importance to topics than to time).
Within the methods based on ranking, the differences are also quite small, although the best
method is always a variant of Borda (depending on the metric, either direct Borda or Reciprocal
Borda is better) and Condorcet produces the worst results.
To assess the robustness of our main findings, we have used a statististical significance test,
namely the McNemar test [27, 14]. This is a non-parametric test for paired data and we have
applied it for the accuracy@1 metric. Essentially it compares, for each article in the test set, the
result obtained by two different models, which in our case may be success or failure (i.e. the top
ranked journal is the true journal where the test article was published or not), builds a 2 × 2
contingency table and computes a statistics related to the number of cases where the two models
get different results. We selected a confidence level of 90%. For the pairwise comparisons we
have used the best score-based and ranking-based fusion models (CombSUM and BordaPROD)
as well as the Topical and Temporal components and the baselines Atomic and Monolithic.
The results of these pairwise tests (the p-values) are displayed in Table 2.
These results confirm that CombSUM is the best method, showing statistically significant
differences with all the other methods. BordaPROD is the second best, although its differences
with Topical and Temporal are not significant (but they are with all the other methods). Topical
is somewhat better than Temporal (but without significant differences), which in turn is better
than Monolithic and Atomic (having significant differences). Finally, Atomic is significantly
better than Monolithic.
Table 2
p-values of the pairwise McNemar tests.
BordaPROD Topical Temporal Atomic Monolithic
CombSUM 0.0232 0.0652 0.0200 0.0001 1.81e-07
BordaPROD - 0.2507 0.1027 0.0017 6.15e-06
Topical - - 0.3716 0.0001 4.32e-07
Temporal - - - 5.98e-06 0.0001
Atomic - - - - 0.0636
8. Concluding remarks
We have tackled the publication venue recommendation problem by building two content-
based recommender systems using different dimensions: One system is based on a process that
analyzes the articles published in the different venues through latent Dirichlet allocation and
generates homogeneous subprofiles for each venue associated with the different discovered
topics, which are then indexed by an IRS. The other system uses the temporal information of
each published article, by means of a decay function, to modify the output of another IRS. The
lists of recommended venues for publishing a given target article generated by each method are
then combined by using several score-based and ranking-based fusion strategies.
We have carried out experiments with a collection of biomedical journal articles. The results
obtained indicate that both the topical and the temporal recommenders improve the performance
of the baseline models which do not use these types of information. Moreover, all the fusion
strategies further improve the performance, thus showing that the two dimensions, temporal
and topical, are complementary and their combined use is always beneficial.
We have considered the topical and temporal recommenders essentially in a separate way and
then we have combined them by means of fusion strategies. However, for future work it would be
interesting to consider other ways of merging these two dimensions, for example through the use
of time-based topic models [7, 41]; or further subdividing the topical subprofiles into periods of
time, thus obtaining topical-temporal subprofiles, as done in [12] in the context of expert finding;
or creating temporal subprofiles for different periods of time and then further subdividing them
topically by learning separate topical subprofiles within each temporal subprofile, thus obtaining
temporal-topical subprofiles.
Acknowledgments
This work was supported by the Spanish “Agencia Estatal de Investigación” [grant number
PID2019-106758GB-C31]; the Spanish “Programa operativo FEDER Andalucía 2014-2020 de la
Junta de Andalucía y la Universidad de Granada” [grant number A-TIC-146-UGR20]; and the
European Regional Development Fund (ERDF-FEDER).
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