Towards Keyword-based Pull Recommendation Systems
Mar
´
ıa del Carmen Rodr
´
ıguez-Hern
´
andez
1
, Sergio Ilarri
1
, Raquel Trillo-Lado
1
and Francesco Guerra
2
1
Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain
2
University of Modena and Reggio Emilia, Modena, Italy
Keywords:
Keyword-based Search, Recommendation Systems, Mobile Computing, Hidden Markov Model, Information
Retrieval.
Abstract:
Due to the high availability of data, users are frequently overloaded with a huge amount of alternatives when
they need to choose a particular item. This has motivated an increased interest in research on recommendation
systems, which filter the options and provide users with suggestions about specific elements (e.g., movies,
restaurants, hotels, books, etc.) that are estimated to be potentially relevant for the user.
In this paper, we describe and evaluate two possible solutions to the problem of identification of the type
of item (e.g., music, movie, book, etc.) that the user specifies in a pull-based recommendation (i.e., recom-
mendation about certain types of items that are explicitly requested by the user). We evaluate two alternative
solutions: one based on the use of the Hidden Markov Model and another one exploiting Information Re-
trieval techniques. Comparing both proposals experimentally, we can observe that the Hidden Markov Model
performs generally better than the Information Retrieval technique in our preliminary experimental setup.
1 INTRODUCTION
Recommender systems (Jannach et al., 2010; Kantor
et al., 2011; Adomavicius and Tuzhilin, 2005) suggest
(relevant) items to users. The suggestions can help
to solve certain decision-making problems which are
presented to the users, such as which books to buy,
which movies to watch, or which online news to read.
They try to adapt the suggestions to each user individ-
ually, based on his/her preferences.
Existing pull-based (reactive) recommendation
approaches usually assume that the type of item
needed by the user is accurately determined by us-
ing some external procedure. For example, the user
could select an option from a list of predefined types
of items. Although this direct selection is very precise
and it may be practical in some contexts, we argue
that this approach lacks generality and is quite rigid
for the user. For example, in a dynamic environment
where new data sources could appear or disappear at
any time, it may be inconvenient or difficult to have a
predefined set of available types of items collected in
a static list of options. Moreover, a solution based on a
selection among a list of options could be tedious and
uncomfortable for the user, who may be forced to use
a specific vocabulary and patiently navigate menus.
Therefore, we advocate offering a keyword-based
interface to allow users to freely express their needs.
For the detection of the type of item requested by a
user we consider the existence of a database that con-
tains information about the different types of items
available, according to the Entity-Relationship (E/R)
schema shown in Figure 1. A part of the schema
(“item datasets”) focuses on the items available: an
item may have a type and can be described through
a list of features (name-value pairs). Another part
of the schema (“ratings”) stores information about
the ratings of the items: for each combination user-
context-item we may have a specific rating (if avail-
able), and each context is characterized by a set of
variables. By associating context information to each
rating, the E/R schema acknowledges that the con-
text of the user has an impact on the user’s per-
ception of the usefulness of different items, as ad-
vocated by the so-called Context-Aware Recommen-
dation Systems (CARS) (Adomavicius and Tuzhilin,
2011); a typical influential context variable is the lo-
cation of the user (Levandoski et al., 2012). The dou-
ble rectangles in the E/R diagram indicate weak en-
tity types (Teorey et al., 1986) and the diamonds are
oriented towards the regular entity type/s they depend
on. In this paper, we focus only on the “item datasets”
fragment of the E/R schema.
While intensive research has been performed in
the area of keyword-based searching, the use of
keyword-based systems as a support for recommen-
Rodríguez-Hernández, M., Ilarri, S., Trillo-Lado, R. and Guerra, F.
Towards Keyword-based Pull Recommendation Systems.
In Proceedings of the 18th International Conference on Enterpr ise Information Systems (ICEIS 2016) - Volume 1, pages 207-214
ISBN: 978-989-758-187-8
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
207

Figure 1: Entity-Relationship diagram modeling data for a pull-based context-aware recommendation system.
dation systems is rather scarce. We believe that
keyword-based searching approaches for relational
databases cannot be directly applied to help users
define their interests for a recommendation system.
The reason is that those approaches focus on a differ-
ent problem; for example, techniques such as those
proposed in (Bergamaschi et al., 2010; Bergamaschi
et al., 2013) are specialized in queries that retrieve in-
formation from several tables at the same time (i.e.,
join queries), and therefore their adaptation or direct
application in scenarios where simpler queries are of-
ten needed may result in inefficient solutions. In or-
der to understand the recommendation needs of the
user by using keywords, the recommendation system
must be able to interpret the semantic meaning of the
keywords typed by the user and identify the type of
item to recommend. For this purpose, it might need
to take into account semantic relations such as syn-
onyms, similar or related keywords (e.g., restaurant –
hungry, coffee shop – bar – sleepy), etc.
The current goal of this paper is to start exploring
possible approaches for the identification of the type
of item requested by a user in a pull-based recommen-
dation process (del Carmen Rodr
´
ıguez-Hern
´
andez
and Ilarri, 2015), by using keywords in the user re-
quest. For example, if a user introduces in the sys-
tem the keywords “place to eat” the system must be
able to interpret that the user is searching items of
the type “restaurant” (or similar types of items, like
“bars”, if available) without the need to choose the
item type from a list previously defined in the sys-
tem. In this paper, two alternative methods are consid-
ered: a solution based on the Hidden Markov Model
(HMM) (Rabiner, 1989) and a solution based on the
application of traditional Information Retrieval (IR)
techniques (Salton and McGill, 1986).
The structure of the rest of this paper is as follows.
Section 2 discusses some related work. In Section 3
and 4, we present the HMM and IR approaches, re-
spectively. In Section 5, a set of experiments is con-
ducted to evaluate both proposals. Finally, we con-
clude the paper and present some lines of future work
in Section 6.
2 RELATED WORK
In the area of Information Retrieval (IR) (Salton
and McGill, 1986), where generally the data are
unstructured (e.g., searching relevant documents
in the Web), the problem of keyword-based query
answering by using an inverted index (Zobel and
Moffat, 2006) has been studied. For structured data,
the field of keyword-based search has started to
emerge more recently (Chakrabarti et al., 2010).
There are several systems that support keyword-
based searching over structured data sources, such as
BANKS (Aditya et al., 2002), DBXplorer (Agrawal
et al., 2002), DISCOVER (Hristidis and Papakon-
stantinou, 2002), KEYRY (Bergamaschi et al.,
2011), QUEST (Bergamaschi et al., 2013), and
KEYMANTIC (Bergamaschi et al., 2010). As
an example, EASE (Li et al., 2008) is a generic
keyword search method which allows to index
and query large collections of heterogeneous data
(unstructured, semi-structured, and structured data).
The authors of EASE extended the tradi-tional
inverted index in order to provide keyword-based
search, and additionally they proposed a novel
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
208

ranking mechanism to improve the search effectiveness .
Recommendation Systems (RS) (Jannach et al.,
2010; Kantor et al., 2011; Adomavicius and Tuzhilin,
2005) have been a main focus of research, as these
systems gradually reduce the existing information
overload (information available on the Internet, data
provided by devices/sensors of different types or other
users, etc.), by recommending to the users person-
alized items of interest (e.g., movies, music, books,
news, images, etc.) based on their preferences.
However, in the field of RS, only a few works are
marginally related to keyword-based searching. For
example, two methods were studied for personaliz-
ing and improving the results of a social search en-
gine (Shapira and Zabar, 2011), by using collabo-
rative users’ knowledge and integrating information
from the user’s social network; the proposed engine
provides traditional keyword-based search function-
alities. That work belongs to the field of IR, thus
considering unstructured data sources, and applies
recommendations to improve and customize the user
experience. For movie recommendations, a hybrid
system that alleviates the noise and semantic ambi-
guity problems present in keyword and tag repre-
sentations of movies and user preferences was pro-
posed (Stanescu et al., 2013). That proposal combines
collaborative filtering and content-based recommen-
dation techniques. As a final example, a study has
been developed focused on improving the scalability
and efficiency of a Big Data environment (Singam and
Srinivasan, 2015). Specifically, the authors exploit
keywords to indicate the preferences of users from a
keyword candidate list, in order to generate appropri-
ate recommendations based on a hybrid filtering algo-
rithm.
As opposed to previous works, we specifically fo-
cus on the problem of correctly identifying the type
of item required by a user when using a standard pull-
based recommendation system. This first study in that
direction represents a preliminary step forward for the
development of complete and generic recommenda-
tion frameworks (del Carmen Rodr
´
ıguez-Hern
´
andez
and Ilarri, 2015).
3 HMM APPROACH
A Hidden Markov Model (Rabiner, 1989) can be de-
fined as a triple A, B, π, where:
• A = a
i j
are the state transition probabilities.
• B = [b
j
(T )] are the observation probabilities (for
each observation symbol T) at each state j.
• π = [π
i
] are the initial state probabilities.
There are mainly two basic problems associated
to a Hidden Markov Model (Rabiner, 1989) which are
relevant for the problem tackled in this paper:
1. Problem 1. Given the observation sequence
O = O
1
, O
2
, . . . , O
T
, and the model λ, how
do we choose a corresponding state sequence
Q = q
1
, q
2
, . . . , q
T
with the highest probability
P(Q|O, λ) (i.e., which best “explains” the obser-
vations)?
2. Problem 2. How do we adjust the model parame-
ters λ = (A, B, π) to maximize P(O|λ)?
According to the existing literature, the first prob-
lem (i.e., the problem of finding the most likely ex-
planation for an observation sequence) can be solved
efficiently using the Viterbi algorithm (Forney, 1973;
Lou, 1995). To adapt that algorithm for our purposes,
we have to define the following structures:
• Q is the set of states, which will be composed of
the feature names (that characterize the items) and
the item type.
• O is the set of observations, which will be the item
types, as well the names and values of the item
features.
As an example, we show in Figure 2 a fragment
of the HMM proposed for a dataset InCarMusic (Bal-
trunas et al., 2011). The idea is the same for any other
dataset.
Figure 2: HMM model for the InCarMusic database.
From a reference database containing data on the
domain (“item datasets” fragment in Figure 1), we ex-
tract a file “observations.txt” that contains the values
and the names of the item features (e.g., title, artist
and category in the case of Figure 2) and the item
types (e.g., music in the case of Figure 2). More-
over, we extract a file “hmm model.dat” with a spe-
cific structure. Considering the example of Figure 2,
the structure of the file for three states (e.g., music-
title, music-artist and music-category) and several ob-
servations (e.g., title, t
1
, t
2
, artist, a
1
, a
2
, a
3
, category,
c
1
, c
2
, music) is displayed in Figure 3.
In the model λ, each state contains the state tran-
sition probabilities A, the observation probabilities B,
Towards Keyword-based Pull Recommendation Systems
209

Figure 3: Example of a “hmm model.dat” file structure.
and the initial state probabilities π. At the moment,
by default, the probability values by state of the vec-
tor B are equally distributed on all the observations,
dividing one by the number of terms related to the
current state. Similarly, the state transition probabili-
ties A have the same values for all the states, obtained
by dividing one by the number of states. The initial
state probabilities π are determined similarly. Never-
theless, our system supports the manual modification
of the otherwise-equal values: the developer of a rec-
ommendation system can provide higher weights for
certain elements that he/she considers more relevant,
and the weights of the remaining elements will be re-
adjusted to ensure that the sum of all the probabilities
is still equal to one.
The keyword-based pull recommendation process
proposed is presented in Figure 4, which summarizes
the following sequence of steps:
Figure 4: Keyword-based recommendations using HMM.
1. Input of the query: the user introduces the key-
words as the input query in the Graphical User
Interface (GUI).
2. Query pre-processing: the keywords are prepro-
cessed by using the extension of an analyzer of
Lucene, which applies the following filters:
• Quotation tokenizer: it parses the query re-
specting the numbers and the double quotes.
• Standard filter: it applies a standard tokenizer
that parses the query into different types based
on a grammar; for example, it splits words at
punctuation characters, it removes punctuation,
it splits words at hyphens (unless there is a
number in the token), and it recognizes email
addresses and internet host names as a single
token.
• Lower case filter: it normalizes the text of the
token by converting it to lower case.
• Stop filter: it removes stop words from the to-
ken streams, by using an input file containing
stop words.
• Snowball filter: it applies a filter that stems
words using a Snowball-generated stemmer.
3. Application of the Viterbi algorithm: given the
keywords as the observation sequence O and
the HMM model λ, it allows determining the
state sequence Q with the highest probability
(e.g., music-title, music-artist, music-category,
book isbn, book title, book author, book year,
book publisher, etc.).
4. Selection of the type of item: the type of item
that the user needs would be determined by the
highest-frequency state sequence (obtained in the
previous step).
5. Filtering of the database: the database containing
the different datasets is filtered by considering the
type of item identified in the previous step (e.g.,
film, music, book, or concert). The data filtered
will be used by the pull recommendation algo-
rithm.
6. Application of the pull recommendation algo-
rithm: it allows obtaining items of interest as an
answer to the query submitted by the user, by
applying any existing recommendation algorithm
desired.
7. Display of the items recommended: a list of items
recommended are provided to the user.
4 INFORMATION RETRIEVAL
APPROACH
A second solution to consider to solve our general
problem is the use of Information Retrieval (IR) tech-
niques (Salton and McGill, 1986). In this case, the
index of the retrieval engine contains a certain num-
ber of documents, whose content is automatically ob-
tained from the databases that store the datasets (see
Figure 1). Each document is named with the item type
and the feature names. For example, for the dataset
InCarMusic (Baltrunas et al., 2011), the document
names to index are “music title”, “music artist”, and
“music category”. The content of each document is
composed of the values of the features (e.g., the artist
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
210

names, the music categories, and the music titles), the
item type (e.g., music), and the names of the features
(e.g., title, artist, and category). The structure of the
documents to index is displayed in Figure 5.
Figure 5: Example of the structure of the documents to in-
dex with the IR approach.
In general, the keyword-based pull recommenda-
tion process performs the following steps (see Fig-
ure 6):
Figure 6: Keyword-based recommendations using IR.
1. Input of the query: the user introduces the key-
words as the input query in the Graphical User
Interface (GUI).
2. Query pre-processing: the keywords are prepro-
cessed by using the same procedure described in
Section 3.
3. Application of the Information Retrieval algo-
rithm: given the input keywords, the system
searches in the index the k documents that are
most relevant to the query.
4. Selection of the type of item: the item type that the
user needs would be the item type corresponding
to the most relevant document (of the ranked list
obtained).
5. Filtering of the database: the database containing
the different datasets is filtered by considering the
type of item identified in the previous step (e.g.,
film, music, book, or concert). The data filtered
will be used by the pull recommendation algo-
rithm.
6. Application of the pull recommendation algo-
rithm: it allows obtaining items of interest as an
answer to the query submitted by the user, by
applying any existing recommendation algorithm
desired.
7. Display of the items recommended: a list of items
recommended are provided to the user.
5 EXPERIMENTAL EVALUATION
In this section, we present the experimental evaluation
that we have performed to evaluate the two methods
proposed. In Section 5.1, we describe the datasets that
we use for the evaluation. In Section 5.2, we present
the keyword-based queries that are evaluated. Then,
we present the experimental settings and the evalua-
tion results.
5.1 Datasets
We consider the following six datasets: LDOS-
CoMoDa (Ko
ˇ
sir et al., 2011), InCarMusic (Baltrunas
et al., 2011), Book-crossing (Ziegler et al., 2005),
ConcertTweets (Adamopoulos and Tuzhilin, 2014),
RCdata (Vargas-Govea et al., 2011) and Frappe (Bal-
trunas et al., 2015). In Table 1, some statistics related
to the items of these datasets are described.
Table 1: Basic statistics of the datasets.
LDOS-CoMoDa InCarMusic Book-crossing ConcertTweets RCdata Frappe
Number of items 2513 139 271084 50971 130 4082
Number of attributes 8 7 7 8 25 10
In order to represent the HMM model, we used
the item types, the feature names, and the feature
values of the six datasets considered. However, we
only chose the most appropriate features. Specif-
ically, we ignored some features (name and val-
ues) of the following datasets: InCarMusic (e.g.,
album, mp3url, description, and imageurl), Book-
crossing (e.g., image-URL-S, image-URL-M, and
image-URL-L), ConcertTweets (e.g., URL), RCdata
(e.g., fax, URL, and the-geom-meter), and Frappe
(e.g., icon, description, and short description). We de-
cided to ignore these features because they do not pro-
vide useful information. To experiment with datasets
of equal size, we limit the number of instances con-
sidered from each dataset to 1000.
Considering the six datasets mentioned, the
HMM model λ is composed of 52 states (e.g.,
film director, music artist, book title, concert date,
restaurant address, application category, etc.). These
states are the combination of the item types (e.g., film,
music, book, concert, restaurant, application) and the
feature names (e.g., director, artist, title, date, address,
category) of the six datasets.
5.2 Queries
The two methods proposed (the one based on HMM
and the one based on traditional IR) were evaluated
by using 45 queries (see Table 2). Notice that some
queries actually correspond to item types that are
not available in the datasets considered. For those
Towards Keyword-based Pull Recommendation Systems
211

queries, the best possible output is “other”, that rep-
resents a type of item not identified. For example,
queries with identifiers from 30 to 35 explicitly in-
clude elements that are not part of the contents of the
datasets.
Table 2: List of queries for evaluation.
Query Id Original query
1 films similar to “toy story”
2 a romantic movie of the year 2009
3 videos of the director “walter lang”
4 the english film titled monkeys
5 a movie of the actor “diane ladd”
6 a music of the singer giovanni
7 rock song
8 music titled “f
¨
ur immer”
9 song of a rock artist
10 songs like “potato head blues” by “louis armstrong”
11 books about “seabiscuit”
12 books similar to ”fast women” by the author “jennifer crusie”
13 publications with an isbn number similar to 195153448
14 documents by the publisher scholastic
15 books with title “urban etiquette” and publisher ”wildcat canyon
16 concert of the band “iron maiden”
17 musical group that will play on ”02/04/2014” in Madrid
18 concerts in the venue “twickenham stadium”
19 the band direction in the state germany
20 concerts like “cattle decapitation” in ”cellular center”
21 publications with an isbn
22 place to eat
23 lodging in Modena
24 romantic melody
25 upcoming soccer matches in Barcelona
26 a recent horror movie
27 songs of movies
28 readings about movies
29 books about singers
30 self-help documents
31 romantic movie in 1949
32 policy documents of 1930
33 festivals in the region of the Holguin
34 movies that were premiered in 1927
35 documents of the Antarctica of 1908
36 restaurant with bar and permit smoking
37 place for dinner with an ambience familiar and low price
38 places opened in the hours of “12-00-22-00” to have lunch
39 restaurants with the name “taqueria el amigo”
40 french food and with “MasterCard Eurocard” payment
41 applications of photography
42 mobile applications developed by yahoo
43 chats similars to “whatsapp messenger”
44 “sport game” with many downloads
45 an apk similar to “Angry Birds” and language es
5.3 Implementation and Hardware
For the implementation of the HMM-based method,
we used the Hidden Markov Model function-
alities provided by the popular library Apache
Mahout (http://mahout.apache.org/). Similarly,
for the indexing of the documents for the IR-
based method, we used Apache Lucene 2.4.0
(https://lucene.apache.org/). As explained in Sec-
tions 4 and 4, Lucene is also used for preprocessing
(of input keywords and/or documents) in both meth-
ods.
Regarding the hardware, we used a standard stan-
dalone computer with the following features: Intel
Core i5-2320 processor with 3 GHz and 16 GB of
RAM, running Windows 7. We evaluated the perfor-
mance of the proposals, although we omit the details
due to space constraints. The latency is on the or-
der of a few milliseconds (slightly higher for the IR
approach) and the average memory consumption is
around 9.5 MB (HMM) or 2.85 MB (IR).
5.4 Accuracy
The first proposed solution (based on HMM) com-
putes the most likely state sequence matching an ob-
servation sequence given an HMM model. The sec-
ond proposal (based on IR) searches the keywords
in the query in the index of documents and returns
a ranked list of hits. According to the values obtained
of precision, recall and F1 measure in Tables 3 and 4,
the HMM model performs better than the IR model in
the experimental setup considered.
Nevertheless, it should be noticed that the IR ap-
proach is able to retrieve a ranked list of possible item
types, but the HMM approach is only able to return
one. Retrieving a top-K list could be interesting, as
the user could quickly correct the item type identified
as the most likely one if it is not correct. Although
the Viterbi algorithm allows querying the probability
of the most-likely sequence of states, it is not possible
to retrieve the probability of all the possible sequences
of states (which would be required in order to obtain
a ranking).
Table 3: Evaluation of the HMM model.
Item type Precision Recall F1
film 1.0 0.75 0.86
music 1.0 0.86 0.92
book 1.0 0.64 0.78
concert 0.71 0.83 0.77
restaurant 0.83 1.0 0.91
application 1.0 1.0 1.0
other 0.25 0.67 0.36
Average 0.83 0.82 0.80
Table 4: Evaluation of the IR model.
Item type Precision Recall F1
film 0.67 0.75 0.71
music 1.0 0.43 0.6
book 0.78 0.64 0.7
concert 0.83 0.83 0.83
restaurant 0.71 1.0 0.71
application 0.56 1.0 0.86
other 0.0 0.0 0.0
Average 0.65 0.66 0.63
A possible problem with the HMM model is how
to determine suitable probability values when the ob-
servation vector size is very large; potentially, it could
happen that very small probabilities could be rounded
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
212

to zero if the global probability values are shared
among many different possible values. Besides, bet-
ter methods to assign the probabilities (by default we
consider a proportional distribution/sharing) could be
considered. Despite these concerns, the performance
results obtained in the datasets evaluated so far are
quite good.
5.5 Impact of the Number of Instances
We conducted another experiment with the aim of an-
alyzing the impact of increasing the number of in-
stances in the datasets on the performance of the two
methods analyzed. For this experiment, we specif-
ically selected the datasets Book-crossing, Concert-
Tweets, and Frappe, which contain the larger number
of instances (see Table 1). For each dataset, we con-
sidered four different versions (subsets of the origi-
nal datasets) with an increasing number of instances
(1000, 2000, 3000, and 4000 instances, respectively).
Then, we evaluated the performance (precision,
recall, and F1-measure) of both models, as shown in
Figures 7, 8, and 9. As shown in the figures, increas-
ing the number of instances in the datasets in general
leads to a decrease in the performance of both meth-
ods, but it is quite moderate and not very significant
beyond 2000 instances per dataset. Again, in general
the HMM-based method performs better than the IR-
based approach.
Figure 7: Average precision of both approaches.
Figure 8: Average recall of both approaches.
Figure 9: Average F1-measure of both approaches.
6 CONCLUSIONS AND FUTURE
WORK
In this paper, we have presented two methods for the
identification of the type of item required by a user
in a pull-based recommendation process. Up to the
authors’ knowledge, this is the first work that focuses
on the problem of explicitly applying keyword-based
techniques in a recommendation system scenario.
Despite the interest of the results obtained, this
study is still preliminary and we can therefore envi-
sion several avenues of improvement. For example,
a thesaurus can be used to obtain synonyms of key-
words provided by the user. Similarly, key-phrases
could be considered rather than only keywords. Be-
sides, more experiments should be performed with a
larger number of datasets (types of items) and a larger
number of items. In particular, the problem that may
arise if the sizes of the datasets are very different from
each other should be analyzed, as a bias in favor of
larger datasets may appear in the identification of the
type of item.
ACKNOWLEDGEMENTS
This work has been supported by the CICYT project
TIN2013-46238-C4-4-R, DGA-FSE, and the Key-
stone COST Action IC1302.
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