=Paper=
{{Paper
|id=Vol-3651/DARLI-AP_paper9
|storemode=property
|title=In Time Recommendations through an Associative Classifier and LookBackApriori: a Case Study
|pdfUrl=https://ceur-ws.org/Vol-3651/DARLI-AP-9.pdf
|volume=Vol-3651
|authors=Anna Dalla Vecchia,Niccolò Marastoni,Elisa Quintarelli
|dblpUrl=https://dblp.org/rec/conf/edbt/VecchiaMQ24
}}
==In Time Recommendations through an Associative Classifier and LookBackApriori: a Case Study==
https://ceur-ws.org/Vol-3651/DARLI-AP-9.pdf
In time recommendations through an Associative Classifier
and LookBackApriori: a case study
Anna Dalla Vecchia1,*,† , Niccolò Marastoni1,† and Elisa Quintarelli1,†
1
Dept. of Computer Science, University of Verona, Verona, Italy
Abstract
Recommender systems are becoming essential tools in many scenarios, as they help users extract hidden knowledge and useful
insights from datasets. In many real domains, the temporal order between events, combined with their contextualization,
improves the accuracy of provided suggestions. In this paper, we introduce a framework designed to mine personalized,
in time, contextual, and explainable sequential rules useful to provide recommendations for a predefined target parameter.
Specifically, this framework is composed of the 𝐿3 Associative Classifier and LookBackApriori, a modification of Apriori
algorithm. Our proposal takes historical data and contextual information as input and generates two sets of rules: the first set
comprises rules that allow enhancement of the target parameter, and the second makes it worse. The proposed technique is
applied to a real-world scenario involving data collected by Fitbit wearable devices to improve the user’s sleep score after
performing fitness activities in different contexts. The idea has been evaluated on two real datasets, and the results confirm
the positive effects of the combination of 𝐿3 with LookBackApriori.
Keywords
Recommendations, Associative classifier, Explanation, Data Mining
1. Introduction conditions in the user location and holidays.
More in detail, our use case is based on data gathered
The widespread popularity of sensors and wearable de- with Fitbit and focuses on suggesting the intensity of
vices, like smartwatches and fitness trackers, has in- physical activities and rest periods to carry out during
creased the amount of available data that can be lever- the current day to sleep better. This is done by mining
aged to monitor and enhance various aspects of their the historical contextualized physical activities during a
users’ well-being. Such devices are often equipped with specified temporal window, which represents the number
intuitive apps for activity tracking that mainly provide of consecutive observation days taken into account.
aggregate parameters and trend analysis, thus leaving We use two datasets consisting of activity logs from
room for more personalized and insightful suggestions to Fitbit wearable devices: PMDataset [1] and a Custom
raise the end-users’ awareness about what affects certain dataset. The latter has been collected from four will-
monitored parameters and habits. ing participants in the past two years to integrate more
To achieve advanced insights, historical data, possi- specific information about the user context.
bly integrated with external information describing the The main aim of this paper is the construction of a
user context, needs to be analyzed for each user to offer novel recommender system that combines the strengths
tailored and context-aware suggestions to improve their of two algorithms: the 𝐿3 associative classifier [2, 3] and
life beyond generic recommendations. LookBackApriori (LBA) [4, 5, 6]. The first one allows us
In this work, we propose a framework that aims to to predict a specific target parameter based on associative
give personalized, and in time, contextual suggestions to classification. It takes as input all the historical physical
a specific user to improve a target parameter (e.g., sleep activity and the related contextual information (i.e., the
quality) along with an explanation about the provided context at the time the physical activity was performed)
suggestions. To achieve this goal we integrate monitored and outputs the predicted sleep score. We leverage the
data with contextual information, e.g., current weather second algorithm to provide an explainable recommen-
dation about what activity to do and what to avoid to
Published in the Proceedings of the Workshops of the EDBT/ICDT 2024 increase the predicted sleep quality and not decrease it.
Joint Conference (March 25-28, 2024), Paestum, Italy With this framework, we overcome the limitations of
*
Corresponding author.
† the two algorithms and, in particular, the state explo-
These authors contributed equally.
$ anna.dallavecchia@univr.it (A. Dalla Vecchia); sion problems of LBA when managing wide temporal
niccolo.marastoni@univr.it (N. Marastoni); windows are less severe in L3 . In addition, LBA allows
elisa.quintarelli@univr.it (E. Quintarelli) the production of explainable recommendations; indeed,
0000-0001-7026-5205 (A. Dalla Vecchia); 0000-0001-6988-1203 since LBA is based on Apriori, it mines sequential rules
(N. Marastoni); 0000-0001-6092-6831 (E. Quintarelli) that contain in their antecedent the explanation of the
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
CEUR
Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
provided sleep score present in the consequent.
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Prediction
Historical Data
Associative OutC R+
+ Ordering
Classifier
Contextual Data and
Splitting R-
Contextual Data
Rule Generaor R
at time zero
Explanation
Data
at time zero
Figure 1: The main components in our framework
The document is organized as follows: Section 2 out- LookBackApriori The second part of our framework
lines the architecture of our recommender system, while uses the LBA algorithm, which was initially proposed
Section 3 presents the case study. Section 4 reports the in [4] and extended in [5, 6]. It is an algorithm based
validation of the proposed framework, and Section 5 ex- on Apriori that mines totally ordered sequential rules
plores related work. Finally, Section 6 summarizes the and provides timely and explainable recommendations.
main contributions and outlines future directions. More in detail, LBA mines association rules where the
antecedent is a sequence of itemsets that represent past
events or actions by the user with an explicit relative
2. Architecture order w.r.t. the current itemset (representing current day
events and actions).
The framework proposed in this paper combines an as-
For our specific scenario related to wearable devices, a
sociative classifier, 𝐿3 , to predict the value of a target
rule can be formalized as follows:
parameter (e.g., the sleep score for the current day, the
stress level) and an algorithm based on Apriori, called 𝑟 : 𝐼−(𝜏𝑤 −1) ∧ · · · ∧ 𝐼−2 ∧ 𝐼−1 ∧ 𝐼0𝑓 → 𝐼0𝑠 [𝑠𝑖 , 𝑐𝑖 ]
LookBackApriori (LBA), to generate timely and explain-
able contextual recommendations helpful to suggest what The rule 𝑟 consists of a sequence of itemsets, each rep-
to do to improve the predicted value (i.e. the fitness ac- resenting either fitness activities (𝐼 𝑓 ), sleep quality (𝐼 𝑠 ),
tivities to undertake in the current day to increase the or both (𝐼), for a specific day, where 0 represents the
sleep score, whenever it is possible). current day. The parameter 𝜏𝑤 defines the temporal win-
dow, that is, the number of consecutive days that the
Associative classifier The first part of our framework algorithm can consider and thus may be present in a rule.
comprises the 𝐿3 associative classifier [2, 3]. 𝐿3 uses The antecedent can contain all the itemsets up to 𝐼−1
a technique of lazy pruning to discard those rules that (the day before the current one) or some of them (i.e., the
classify training data incorrectly. sequence may be incomplete), while only the physical
Then, the classification of unlabeled data is executed activity data is present for the current day. Indeed, this
in two steps: first, by considering a subset of high-quality itemset, 𝐼0𝑓 , represents the physical activity suggested to
rules for the classification process, and second, by adding the user by our framework to improve their sleep score
a larger set of rules when it fails to find rules for certain for the same night, represented in the consequent as 𝐼0𝑠 .
data points. The associative classifier 𝐿3 is used for historical data
In the green section of Fig. 1, we show the classifier processing and prediction to address memory-related is-
component, which is employed for predicting the value sues faced by LBA that stem from the size of the input.
of a target parameter, represented in our scenario by the After the sleep score prediction step, LBA is used to pro-
sleep score for the current day. It takes historical data vide explainable positive and negative recommendations
(i.e., the user’s log of their fitness activities and sleep thanks to the form of the mined rules. To this end, the
scores) as input and integrates it with past contextual antecedent contains the sequence of events that will lead
information, including the context of the current day at to the sleeping score in the consequent; thus, it provides
the time of the prediction. an explanation for the recommendation.
Three possible rules mined by the LBA Algorithm are
the following:
From both datasets, we make use of the logs about
"light", "medium", and "heavy" activity, along with rest
𝑟1 : {𝐻𝐴 : 3, 𝐿𝐴 : 2}−1 ∧{𝐻𝐴 : 3, 𝐿𝐴 : 2}0 → {𝑆𝐿 : 1}0
periods and the sleep score for each day. Fitbit records
𝑟2 : {𝐻𝐴 : 2, 𝑅 : 3}−2 ∧ {𝐿𝐴 : 1}−1 → {𝑆𝐿 : 3}0 these features as minutes spent in each activity type; thus,
𝑟3 : {𝐻𝐴 : 2, 𝑅 : 3}−2 ∧ {𝑀 𝐴 : 1}0 → {𝑆𝐿 : 3}0 we discretize them to obtain categorical data as described
in [4]. During this discretization process, the activity
𝑟1 states that if yesterday the user performed a high levels and sleep scores are further split into three sub-
level of heavy physical activity (𝐻𝐴 : 3) and a medium levels according to set thresholds, e.g., a heavy activity
level of light activity (𝐿𝐴 : 2), and today they perform (𝐻𝐴) can be encoded into three possible labels: 𝐻𝐴 :
the same activities, the resulting sleep score will have a 1, 𝐻𝐴 : 2, and 𝐻𝐴 : 3. These represent, respectively, a
low value (𝑆𝐿 : 1). low level, medium level, and high level of heavy activity,
𝑟2 is an incomplete rule since it does not contain informa- all decided by the amount of time spent undertaking the
tion about the physical activity the user should perform specific physical activity during the day.
during the current day. Although the rule is valid, it is Regarding the context, for both datasets, we also have
not helpful for making a recommendation to improve information on whether a day falls on a weekend (𝑊 𝐸)
sleep quality, as it does not have any itemset labeled 0 in or not (𝑊 𝐷).
the antecedent. In addition, for the Custom dataset, we integrate the
𝑟3 is another incomplete rule, but it gives us information information about the user’s vacations (𝑉 𝐴/𝑛𝑜𝑉 𝐴) and
about the physical activity the user should do during the the weather conditions. For this last aspect, we have
current day to sleep well; thus, it can be used to provide simplified its representation as follows: if there has been
a recommendation. rain, snow, fog, or other bad weather conditions, the label
In the orange part of Fig. 1, we show the contribution is 𝐵𝑎𝑑. In all the other cases, it is 𝐺𝑜𝑜𝑑. Regarding the
of LBA to our framework. Firstly, it mines a set of rules temperature, we use the average daily temperature as a
𝑅 using the Rule Generator, which takes as input the feature, and the result is labeled into 𝐶𝑜𝑙𝑑 or 𝐻𝑜𝑡, using
current context, also used by 𝐿3 for the prediction step, the yearly average temperature as a threshold.
and data at the time of the prediction. Secondly, taking
advantage of the label produced by the classifier, the rules
generated are split into two sets: those that improve the 3.1. Practical example
target parameter value w.r.t. the predicted one is labeled
𝑅+ , and those that do not are labeled 𝑅− . Then, the rules
in both sets are ordered according to the completeness of
the rule antecedent, confidence, and support. The rules
t-3 t-2 t-1 t0
recommended to the user are the most complete ones
with the highest confidence and support.
Associative
Classifier t0
t-3 t-2 t-1 t0
3. A case study
Our experiments focus on wearable device data: their Rule Generator R t0
logs contain information about daily physical activity t0 t0
levels and sleep scores. Whenever possible (i.e., when Training
R which
we have enough data about the user), we integrate such improve t0
logs with additional information, e.g., holidays, day of R
t0
Ordering and Splitting
the week, and weather conditions related to the user’s R which
deteriorate t0
location, to better contextualize the gathered fitness and
sleep quality data.
Figure 2: An intuitive workflow of our framework
We consider two datasets for this domain: PMdata [1]
and Custom. PMdata consists of logs from 16 users, 13
male and 3 female, all aged 23 to 60 years old. The data Fig. 2 shows the workflow of the framework in the
was collected for 149 days between November 2019 and specific case of study of this paper.
March 2020. First, we need to decide the dimension of the temporal
The Custom dataset was collected from 4 users specifi- window to consider to make the recommendation. The
cally for this study, the earliest of which started recording temporal window shown in Fig. 2 is 4 days, i.e., the three
in August 2021 and ended in September 2022. The partic- previous days are considered along with the current one.
ipants are evenly split between males and females; their All the information about the past days is fed to the
ages vary from 16 to 55. associative classifier 𝐿3 along with the context. The
The relevance of historical data
Only last timeslot data
Historical data
0.4
0.3
Accuracy
0.2
0.1
0.0 p01 p02 p03 p04 p05 p06 p07 p08 p09 p10 p13 p14 p15 p16 USER1USER2USER3USER4
User
Figure 3: The importance of historical data for each user
classifier then returns a sleep score for the current day. • testing the efficacy of the proposed framework
The rules generated during the training phase state on 𝐿3 w.r.t. to its ability to predict the value of
correlations that are only related to the current day, and the predefined target parameter.
are in the form shown in the purple rectangle, i.e., con-
textual information together with physical activity in the For all the experiments, we have reserved the first
antecedent of the rule and the sleep score in the conse- 80% of the Fitbit logs of each user as a training set and
quent. the remaining 20% of the data for testing. Due to the
Some examples of mined rules are the following: sequential nature of the problem, we cannot randomize
the sampling of the two sets. Thus, we maintain the
𝑟1 : {𝐶𝑜𝑙𝑑, 𝐺𝑜𝑜𝑑, 𝑊 𝐷, 𝐻𝐴 : 1, 𝐿𝐴 : 2}0 → {𝑆𝐿 : 1}0 sequential order based on the timestamp of the logs and
𝑟 : {𝐵𝑎𝑑, 𝑊 𝐷, 𝐿𝐴 : 2, 𝑀 𝐴 : 3} → {𝑆𝐿 : 3} select the last 20% of the dataset for the tests.
2 0 0
Rule 𝑟1 tells us that on a weekday with clear weather, if
the user performs low levels of heavy activity (𝐻𝐴 : 1) 4.1. Relevance of historical data
and medium levels of light activity (𝐿𝐴 : 2), their sleep To conduct this part of the experiments, we use the 𝐿3
score for the same night will be low. Whereas rule 𝑟2 associative classifier to predict the sleep label related to
states that, in the case of a stormy weekday, the user will the current day 𝑡_0.
sleep well after performing medium levels of light activity First, we perform the prediction by selecting only the
(𝐿𝐴 : 2) and high levels of medium activity (𝑀 𝐴 : 3). physical activity and context of the current day as input
Thanks to the sleep score obtained by the associative to the classifier without considering historical data. An
classifier, it is possible to split the rules into those that example of input data for a Custom user is:
increase or decrease the predicted sleep score. At this
stage, the user can explain why their sleep quality may (𝐺𝑜𝑜𝑑_𝑡0, 𝐻𝑜𝑡_𝑡0, 𝑊 𝐷_𝑡0, 𝑉 𝐴_𝑡0, 𝐿𝐴_3_𝑡0,
improve or not by looking at the antecedent of the rules. 𝑀 𝐴_1_𝑡0, 𝐻𝐴_1_𝑡0, 𝑅_2_𝑡0)
This can be interpreted as: on clear weather and hot
4. Evaluation weekdays during a holiday, the user performs a high
To test the validity of our framework, we have performed level of light physical activity, low levels of both medium
several experiments on the following aspects: and heavy activity, and a medium level of rest.
Then, we add the historical data (i.e., physical activity,
• verifying the relevance of historical data and their context, and sleep score of the past days) to the input
context in the ability to predict the value of a used for the first set of experiments.
target parameter, i.e., the sleep score. Fig. 3 depicts the recorded accuracies of these two
• testing the performance of the two algorithms experiments for each user in both PMdata and Custom
used by the framework w.r.t. execution time and datasets. These results show that for 77% of users, using
memory consumption.
historical data instead of only using data from the current Fig. 6 and Fig. 7 depict the elapsed time and the mem-
day improves the accuracy of the classifier. ory consumption of the framework when using data from
Fig. 4 shows that, regardless of the presence of con- one of the users of the Custom dataset, showing the im-
textual information, having historical data improves the portant contribution of 𝐿3 . During the experiments, the
accuracy of most users. The performance of the algo- input is the physical activity log and the available con-
rithm decays quickly as the temporal window increases, textual information of the chosen user. We maintain the
especially in the absence of contextual data. Thus, it same support and confidence and gradually increase the
seems clear that sleep quality does not depend on data temporal windows, i.e., increase the historical data given
that is temporally distant from the current day. in input. LBA shows an exponential trend for memory
consumption and time elapsed, until its memory alloca-
Percentage of users where the historical data is better than the only last timeslot data
Without context
tion fails when the temporal window reaches value 4.
75
70
With context On the other hand, 𝐿3 can manage at most a temporal
65 window of 6, maintaining a relatively constant trend.
Percentage
60
55 User: USER2
50 L^3
45 LBA
2 3 4
Temporal Window
5 6 300
Time (s)
Figure 4: Percentage of users that obtain higher accuracy with 200
the use of historical data, with different lengths of temporal
windows, w.r.t. the use of data of the current day
100
Additionally, the accuracy value obtained by some 0
users increases gradually in the presence of contextual 1 2 3 4 5 6
Temporal window
information as the length of the temporal window in-
creases. One example is shown in Fig. 5, where we can Figure 6: Execution time with different temporal windows
also confirm that sleep does not depend on the activi- for USER2 of Custom dataset.
ties performed six days earlier. We can also observe that
contextual information improves the final result.
User: USER2
Accuracy of the classifier for user p03 with different temporal windows 3500 L^3
0.450 Without Context 3000 LBA
0.425 With Context
2500
Memory (MB)
0.400
0.375 2000
Accuracy
0.350 1500
0.325 1000
0.300 500
0.275 0 1
0.250
2 3 4 5 6
2 3 4 5 6 Temporal window
Temporal Window
Figure 7: Memory usage with different temporal windows
Figure 5: Accuracy trend when we add more historical data, for USER2 of Custom dataset.
comparison with and without context
4.3. Evaluation of the framework
4.2. Time and memory performance
The last set of experiments performed is on the complete
Despite LBA’s intrinsic capability to provide explainable framework, as explained in Sec. 3.1. The idea is to val-
recommendations for achieving a better sleep score, we idate, on real user logs, the prediction of 𝐿3 enriched
still employ the associative classifier 𝐿3 to process his- with the recommendation produced by LBA. In order to
torical data. Thanks to the associative classifier, we can validate the results of the whole framework, the best ap-
process significantly larger volumes of data, e.g., longer proach would be to ask for inputs directly from the users.
sequences of data with their contextual information, with- Due to time constraints, the evaluation of the framework
out the risk of memory errors. Additionally, 𝐿3 is signifi- is strictly empirical.
cantly faster in obtaining the results.
Accuracy of L^3 associative classifier
0.4
0.3
Accuracy
0.2
0.1
0.0 p01 p02 p03 p04 p05 p06 p07 p08 p09 p10 p13 p14 p15 p16 USER1USER2USER3USER4
User
Figure 8: Accuracy of the 𝐿3 associative classifier
As before, we use the first 80% of the dataset to train 5. Related Work
both 𝐿3 and LBA. The first step is setting the length
of the temporal window, which is done empirically by With the spread of smart devices and the availability of
analyzing the experiments in the previous case study. their large datasets, we have the possibility to extract
The input of the classifier is composed of the physical both explicit and implicit knowledge about monitored
activities, contextual information, and sleep score of the parameters. For this reason, there are many intelligent
past days and the contextual information of the current techniques proposed in the literature to improve the cus-
day. The classifier then predicts the sleep score on the tomization of data exploitation. Recommender Systems
current day. (RS) offer suggestions on items, services, or news that
Separately, the rule generator produces a set of rules may interest users and affect their decisions based on
correlating the physical activities and contextual infor- their profile, history, and preferences [7]. For instance, in
mation for the current day (antecedent) and the related [8], the authors develop an RS that can suggest activities
sleep score (consequent). targeted to specific users to improve their health condi-
At this point, a trained classifier and a set of rules exist tions starting from data collected by a Fitbit wearable
for each user. To test the complete framework, we take device. The physical activity information collected by
from the remaining 20% of the dataset a temporal window Fitbit is also used in [9] to correlate daily physical activity
of observation at a time: all the past data, together with levels with predictions of sleep quality. Neither of the
the current context (i.e., the contextual information of mentioned works considers contextual information.
the current day), are used by the associative classifier to In the literature, there are many methodologies for
predict the sleep score. sleep prediction. In particular, [10] introduces an explain-
In Fig. 8, we report both the accuracy of the classifier, able sleep model that exploits the correlation between
without any knowledge about physical activity for the daily activities and sleep quality, providing recommenda-
current dayIt can be noted that the accuracy of 𝐿3 for tions to improve sleep quality. While the outcome of this
most users is less than 0.45. framework aligns closely with our proposal, the approach
The output of 𝐿3 produced is used as a threshold to does not account for sequences of events that occurred
separate the rules mined by the Rule Generator in the in the days leading up to the prediction intended for the
two sets 𝑅+ and 𝑅− of positive and negative recommen- user. Furthermore, the model presented fails to incor-
dations, respectively. Due to the nature of the problem porate external contextual information beyond sensed
at hand, it would not be accurate to use historical data to humidity and temperature. As highlighted in Subsection
check whether the recommendation given by the frame- 4.1, historical data are important to improve the quality
work will actually result in a change in sleep score. of provided predictions.
In the state of the art, contextual information is often
integrated into RS to improve the precision of recommen-
dations. In general, user preferences may vary depending
on the environment and the situation in which they are
acting [11, 12]. Therefore, Context-Aware Recommender and how to improve it when fitness and sleep parameters
Systems (CARS) use contextual information, such as time, are monitored through wearable devices. As future work,
location, and social situation, to add knowledge during we are extending the proposal to other domains, like the
the recommendation process, thus improving the per- correlation of fitness activities with blood glucose levels.
sonalization and the relevance of the suggestion [13]. A
systematic literature review is proposed in [14], where
the authors describe the integration of the context in RS, References
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