Improving Recommendation Quality in Collaborative Filtering by

Including Prediction Confidence Factors

Kiriakos Sgardelis

1 a

, Dionisis Margaris

1 b

, Dimitris Spiliotopoulos

2 c

, Costas Vassilakis

3 d

and

Stefanos Ougiaroglou

4 e

Department of Digital Systems, University of the Peloponnese, Sparta, Greece

Department of Management Science and Technology, University of the Peloponnese, Tripoli, Greece

Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece

Department of Information and Electronic Engineering, International Hellenic University, Thessaloniki, Greece

Keywords: Collaborative Filtering, Rating Predictions, Recommender Systems, Certainty Factors, Algorithm.

Abstract: Collaborative filtering is a prevalent recommender system technique which generates rating predictions based

on the rating values given by the users’ near neighbours. Consequently, for each user, the items scoring the

highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which,

in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each

rating prediction value, prediction confidence factors can be computed. As a result, items having low

prediction confidence factor values, can be either declined for recommendation or have their recommendation

priority demoted. In the former case, some users may receive fewer recommended items or even none,

especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be

recommended by considering both the rating prediction values and confidence factors of predictions, allowing

for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence.

The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining

the number of recommendations for each user.

1 INTRODUCTION

Collaborative filtering (CF) is a prevalent technique

of predicting rating values in recommender systems

(RecSys). It is based on the numeric rating values that

users close to the active user (i.e. his near neighbours

- NNs) have given to the item (e.g. service, product,

etc.) for which the prediction is being computed.

Consequently, the items achieving the highest

prediction values are suggested to the active user,

since their acceptance is of very high probability. The

nearer these numeric predictions are to the real

numeric rating values, the more successful the

RecSys is (Jain et al., 2023; Nguyen et al., 2023).

Let as assume that for a user U, there are two items

candidate for recommendation, i

and i

, where their

https://orcid.org/0009-0008-7113-8855

https://orcid.org/0000-0002-7487-374X

https://orcid.org/0000-0003-3646-1362

https://orcid.org/0000-0001-9940-1821

https://orcid.org/0000-0003-1094-2520

respective CF rating prediction values have been

computed at 4.8/5 and 4.6/5. Typically, a RecSys will

recommend primarily item i

to U, under the premise

that higher rating prediction value denotes higher

probability that the user will like the item. Let us also

assume that the prediction for item i

is deemed of low

confidence (e.g., it has been computed based on a

very low number of ratings, or by ratings contributed

by users which have a relatively low degree of

similarity to the user for which the recommendation

is generated). On the other hand, the prediction for

item i

is deemed of high confidence (e.g., it is based

on 20 “close” NNs’ ratings). In such a situation, it

appears sensible to opt for recommending i

instead

of i

since, while i

has a marginal advantage with

regards to its rating prediction value, there is a high

372

Sgardelis, K., Margaris, D., Spiliotopoulos, D., Vassilakis, C. and Ougiaroglou, S.

Improving Recommendation Quality in Collaborative Filtering by Including Prediction Conﬁdence Factors.

DOI: 10.5220/0013052200003825

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 2024), pages 372-379

ISBN: 978-989-758-718-4; ISSN: 2184-3252

risk that this value is inaccurate, and hence the user

may not actually like the recommended item. On the

contrary, the recommendation of i

can be deemed to

be “safe”. However, a typical RecSys recommends

items by considering only rating prediction values.

Rating prediction confidence factors, associated

with CF prediction accuracy, have been explored

recently (Margaris et al., 2022; Spiliotopoulos et al.,

2022). These research works demonstrated that (1)

the number of NNs participating in the prediction

computation, (2) the item’s mean ratings value and

(3) the user’s mean ratings value, are related to rating

prediction accuracy. Based on these findings, items

having low prediction confidence factor values can be

either declined for recommendation or have their

recommendation priority demoted. In the former case,

some users may receive fewer recommended items or

even none, notably when using a sparse dataset.

In this paper, we present an algorithm that

determines the items to be recommended by

considering both the rating prediction values and

confidence factors of predictions, allowing for

predictions with higher confidence factors to outrank

predictions with higher value, but lower confidence.

The presented algorithm enhances recommendation

quality, while at the same time retaining the number

of recommendations for each user. The proposed

algorithm is evaluated against 5 widely used CF

datasets (including both dense and sparse, in order to

cover all cases). As far as the NN selection is

considered, both the top-k and the correlation

threshold techniques are considered in the evaluation.

The remainder of the paper is structured as

follows: in Section 2 we present the related work. In

Sections 3 and 4 we summarize the foundations of

confidence factors in CF rating prediction and present

the proposed algorithm, respectively. In Section 5 we

present the evaluation results and in Section 6 we

conclude the paper and outline future work.

2 RELATED WORK

The quality of CF recommendations is a field of

major research interest over the last years. The work

in (Alhijawi et al., 2021) employs a genetic algorithm,

in order to customize the prediction process, based on

the active user’s set of NNs. This genetic algorithm

enables the optimal solution discovery, without

exhaustive analysis, since each person is represented

with a vector. Furthermore, with the use of this

algorithm, the active user’s search parallelism is a fast

process, since the fitness function evaluation is totally

independent for each user.

The work in (Y.-C. Chen et al., 2021) introduces

a CF-based RecSys dynamic decay CF, which, based

on the preference of users’ variations, it incorporates

a time decay function. This work extends the human

brain memory concept, to discover the users’ levels

of interest. As a result, the dynamic decay CF

algorithm adjusts the decay function based on

different user interest levels. The work in (Z. Wang,

2023) presents a CF algorithm that targets to enhance

the recommendation accuracy of tourist activities.

This algorithm overcomes sparse data issues, by

taking into account user preferences, as well as by

using the Jeffries-Matusita vicinity metric (Sen et al.,

2019). The work in (Bobadilla et al., 2023) introduces

a Generative Adversarial Network-based algorithm,

which parametrically produces CF datasets. More

specifically, it allows the selection of users, items and

samples number, as well as the dataset’s stochastic

variability. Furthermore, the presented architecture

incorporates a clustering method which transforms

the dense produced samples into sparse and discrete

ones, as well as a Deep Matrix Factorization model

which exports the dense item and user embeddings.

The work in (Fareed et al., 2023) presents a CF

RecSys framework which, in order to produce more

pertinent and precise recommendations, it

incorporates social network information. The

presented framework is based on a user-based CF

algorithm that estimates user vicinity values based on

both their social relations and their item ratings.

Furthermore, this vicinity metric is determined by

synthesizing the two aforementioned factors, while

the respective weights-importance are determined

through an optimization step. The work in (Vuong

Nguyen et al., 2021) introduces a hybrid RecSys

algorithm which overcomes the issues of cold-start

and data sparsity of the user ratings, by combining

word embedding-based content analysis with CF

methods. Been applied on the film domain, this work

focuses on perceiving the gist of the movie plot, using

word embedding techniques of the films’ features,

such as genres, titles, actors, directors, etc.

The work in (R. Wang et al., 2022) introduces a

time-aware CF algorithm with two phases, a dynamic

user preference phase and a deep learning matching

score prediction phase. During the first phase the

time-aware attention mechanism models the short-

term user preferences. In the second phase the user-

item interactions are discovered by deep learning

models. The results of the two aforementioned phases

are combined for predicting the final score.

Still, the exploitation of the concept of rating pre-

diction confidence factors for enhancing the rec-

Improving Recommendation Quality in Collaborative Filtering by Including Prediction Conﬁdence Factors

373

ommendation quality in CF has not received consid-

erable research attention. Recent works have explored

rating prediction factors, based only on the basic CF

information (the user-item-rating tuple), related with

CF rating prediction accuracy. The works in

(Margaris et al., 2022) and in (Spiliotopoulos et al.,

2022) show that the NN number, the item’s mean rat-

ings value and the user’s mean ratings value are re-

lated with rating prediction accuracy in CF, in sparse

and dense datasets, respectively. The work in

(Margaris et al., 2024) exploits these results to

propose an algorithm that utilizes confidence factors

in CF rating prediction, by eliminating rating

predictions having low values of confidence factors

from becoming recommendations. Although this al-

gorithm results in a considerable recommendation

quality upgrade, the recommendation coverage (i.e.,

the percentage of users that at least N recommenda-

tions can be formulated to them, where N is a given

algorithm parameter) is significantly decreased, while

in the cases of the algorithm’s application on (very)

sparse CF datasets (e.g., Amazon-sourced datasets),

the algorithm becomes almost inapplicable.

The algorithm presented in this work is based only

on the very basic CF information, while it also takes

into account both the rating prediction and the

confidence factors values in CF. However, instead of

following the approach undertaken in (Margaris et al.,

2024), i.e., pruning the recommendation candidate

item list retaining only rating predictions with (very)

high confidence, it determines the items to be recom-

mended by considering both the rating prediction

values and confidence factors of predictions, allowing

for predictions with higher confidence factors to

outrank predictions with higher value, but lower

confidence. Hence, the presented algorithm achieves

to both enhance the recommendation quality, while at

the same time retain the number of recommendations

for each user, and as a result it can be applied in every

CF dataset, including both sparse and dense ones.

3 CONFIDENCE FACTORS IN CF

RATING PREDICTION

Contemporary works have studied rating prediction

factors related with CF prediction accuracy. More

specifically, the works in (Margaris et al., 2022) and

in (Spiliotopoulos et al., 2022) showed a positive

association between CF rating prediction accuracy

and the following factors:

(a) F

#NN

, which considers the number of NNs

participating in the prediction computation,

(b) F

Uavg

, which relates to average value of the

user’s ratings for whom the prediction is being

computed, and

Iavg

, which considers the average value of the

item’s ratings for which the prediction is being

computed.

Table 1 summarizes the thresholds of the

aforementioned factors, that a prediction is classified

(i) as a high accuracy one and (ii) as a very high

accuracy one, both in sparse and dense datasets.

Regarding the F

Uavg

and the F

Iavg

factors, we consider

a 5-star rating scale evaluation. These criteria are

exploited by the proposed algorithm to formulate

recommendations. In the next section we present and

analyze the proposed RecSys algorithm in detail.

Table 1: Thresholds of the CF prediction accuracy factors

for classifying predictions.

Factor

High Accuracy

Very High Accuracy

#NN

≥ 2 (sparse) /

≥ 6% (dense)

≥ 4 (sparse)

≥ 15% (dense)

Uavg

[1.0, 2.0] or [4.0, 5.0]

[1.0, 1.5] or [4.5, 5.0]

Iavg

[1.0, 2.0] or [4.0, 5.0]

[1.0, 1.5] or [4.5, 5.0]

4 THE PROPOSED ALGORITHM

As noted above, the algorithm proposed in this paper

determines the items to be recommended by

considering both the rating prediction values and

confidence factors of predictions, allowing for

predictions with higher confidence factors to outrank

predictions with higher value, but lower confidence.

Considering the formulation of the initial

recommendation candidate list (ICRL), in this paper,

we adopt the approach followed by many works

(Felfernig et al., 2018; Margaris et al., 2020; Trattner

et al., 2024), where the items achieving a rating

prediction value in the top 30% of the rating range

(i.e., 3.5/5 for the 5-star rating scale) are considered

eligible for recommendation to the users.

The proposed algorithm essentially redefines the

step of ranking the items to be recommended in CF

RecSys. More specifically, instead of simply ranking

the items that pass the recommendation threshold (the

top 30% of the rating range, as mentioned above) in

descending order of their rating prediction value, the

algorithm considers both the rating prediction value

and the confidence estimation associated with the

computation of this value. This is realized through the

following steps (for simplicity, we assume a rating

scale [1-5], as in the majority of the CF datasets,

however the algorithm can be easily adapted to

accommodate different rating scales):

WEBIST 2024 - 20th International Conference on Web Information Systems and Technologies

374

Item (rating prediction value, #confidence factors satisfied)

i1 (4.8, 1)

i2 (4.2, 3)

i3 (4.6, 3)

i4 (2.2, 2)

i5 (3.7, 3)

i6 (3.8, 0)

Initial set of rating predictions

ICRL

Top

ICRL

Med

ICRL

Low

Eliminated for

recommendation

i1 (4.8, 1)

i3 (4.6, 3)

i2 (4.2, 3)

i5 (3.7, 3)

i6 (3.8, 0)

i4 (2.2, 2)

ICRL

Top

ICRL

Med

ICRL

Low

Eliminated for

recommendation

i3 (4.6, 3)

i1 (4.8, 1)

i2 (4.2, 3)

i5 (3.7, 3)

i6 (3.8, 0)

i4 (2.2, 2)

Recommend (in this order): i3, i1, i2, i5, i6

Figure 1: Example execution of the proposed algorithm.

• Step 1: The algorithm computes the number of

confidence factors (F

#NN

, F

Uavg

and F

Iavg

) that

are fulfilled by each prediction in the ICRL.

More specifically, for each rating prediction rp

in these subsets, it computes the associated

confidence ranking score CRS

as follows:

CRS

= CRS

rp,F#NN

+ CRS

rp,FUavg

+ CRS

rp,FIavg

where:





 

   

 

with #NNs(rp) denoting the number of NNs

contributing to the computation of rp, and Thr

being the dataset-dependent threshold for

classifying rp as a high accuracy one,

considering the F

#NN

criterion (cf. section 3);





 

 



 



 

 

with 



denoting the average ratings entered by

the user for whom rp has been computed; and





 

 



 



 

 

with 



denoting the average ratings entered for

the item for which rp has been computed.

• Step 2: The algorithm partitions the set of the

items to be recommended into subsets, with

each subset covering a rating prediction range

of 0.5 (or 10% of the rating scale). Effectively,

the following subsets will be formulated:

o ICRL

Top

, which includes the items in the

IRCL having rating prediction values in the

range [4.5, 5]. This list contains the items for

which we can assume that the user will

“definitely” like them;

o ICRL

Med

, which includes the items in the

IRCL having rating prediction values in the

range [4, 4.5). This list contains the items for

which we can assume that the user will

“most probably” like them; and

o ICRL

Low

, which includes the items in the

IRCL having rating prediction values in the

range [3.5, 4). This list contains the items for

which we can assume that the user will

“probably” like them.

• Step 3: The algorithm sorts the items contained

in each subset, in descending order of their

CRS

score. For rating predictions having

equal CRS

score values, the numeric rating

prediction value is used as a tiebreaker.

• Step 4: The recommendation formulation

process begins to select items from ICRL

Top

descending order of the sorting performed in

step 3, until the target number of

recommendations is reached. If the elements of

ICRL

Top

do not suffice, then the elements of

ICRL

Med

are used, and -if needed- the elements

of ICRL

Low

are also considered.

An example of the proposed algorithm is

illustrated in Figure 1, while in the next section, we

assess its recommendation accuracy.

5 EVALUATION

In this section, we detail on the experiments of

recommendation accuracy and recommendation

coverage of the presented algorithm.

5.1 Experimental Settings

Our experimental evaluation utilises five CF datasets,

where the first three are sparse and the last two are

dense, covering thus all sparsity levels. These five

datasets are broadly used in CF research and are

summarized in Table 2.

Considering the user-user vicinity metrics, we

employ the Pearson Correlation Coefficient (PCC)

(Ajaegbu, 2021; Jain et al., 2023). For the NN

selection method, in this work, we employ both the

top-k (KNN) and the correlation threshold (THR)

techniques (Li et al., 2020; Singh et al., 2020). More

specifically, following the approaches of the works in

(Fkih, 2022; Margaris et al., 2024; D. Wang et al.,

2020) in our experiments we set the K=200 and

K=500, regarding the top-k technique, and T=0.0 and

T=0.5, regarding the correlation threshold technique.

Step 2: prediction classification to subsets

Step 3: Sorting of predictions within each subset

Step 4: formulation of recommendation

Step 1: compute number of confidence factors

Improving Recommendation Quality in Collaborative Filtering by Including Prediction Conﬁdence Factors

375

Table 2: The attributes of the datasets used in our

experiments.

Dataset Name

Dataset Attributes

Amazon

Videogames

(Ni et al., 2019)

#ratings: 473K / ratings range: 1-5

#users: 17,500 / #items: 55,000

density: 0,05% (sparse)

Amazon Digital

Music

(Ni et al., 2019)

#ratings: 145K / ratings range: 1-5

#users: 12,000 / #items: 17,000

density: 0.07% (sparse)

CiaoDVD

(Guo et al., 2014)

#ratings: 73K / ratings range: 1-5

#users: 17,600 / #items: 16,000

density: 0.026% (sparse)

MovieLens 100K

(Harper &

Konstan, 2016)

#ratings: 100K/ ratings range: 0.5-5

#users: 600 / #items: 9,700

density: 1.72% (dense)

MovieLens 1M

(Harper &

Konstan, 2016)

#ratings: 1,000K/ ratings range: 1-5

#users: 6,000 / #items: 3,700

density: 4.5% (dense)

Regarding the evaluation metrics, in this work, we

employ (i) the precision of the recommendations, (ii)

their average real numeric rating values and (iii) their

normalized discounted cumulative gain (NDCG),

following the works in (Chin et al., 2022; Krichene &

Rendle, 2020), while regarding the number of

recommended items we use the top-3 and top-5.

In order to generate predictions for the unrated

items in the datasets summarized in Table 2, the five-

fold cross validation process was followed (L. Chen

et al., 2021; Zhang et al., 2021).

5.2 Evaluation Results

5.2.1 Recommendation Coverage

Figure 2 depicts the recommendation coverage

considering the top-3 recommendations, when

employing the KNN technique and having set the

number of near neighbours to 200. This diagram

effectively depicts the percentage of cases where each

algorithm could produce a complete

recommendation, i.e., a recommendation containing

three items. We can observe that the algorithm

proposed in this paper fully maintains the coverage

attained by the plain CF algorithm in all cases, while

the algorithm proposed in (Margaris et al., 2024)

suffers substantial coverage drops. Especially when

considering sparse datasets, coverage drops exhibited

by the algorithm proposed in (Margaris et al., 2024)

range from 75.2% (CiaoDVD) to 82.2% (Amazon

Videogames), rendering this algorithm practically

unusable for these datasets, since in 92-98% of the

total cases, it would be not capable of offering a

complete recommendation.

Figure 2: Recommendation coverage considering the top-3

recommendations, using the KNN technique (K=200).

For dense datasets, the coverage drop of the

algorithm proposed in (Margaris et al., 2024) is again

considerable, ranging from 7.9% to 9.9%.

When the KNN technique is employed with

K=500, the increase of near neighbours leads to more

candidate items, hence the coverage drop observed

for the algorithm proposed in (Margaris et al., 2024)

is lower, ranging from 69.7% to 73% in sparse

datasets and from 4.9% to 16.3% in dense datasets.

However, still the percentage of cases for which a

complete recommendation can be offered is very low

in sparse datasets (2%-15%), therefore the algorithm

proposed in (Margaris et al., 2024) is effectively not

applicable for sparse datasets.

The results obtained under the threshold method

(THR) are similar: for sparse datasets, the algorithm

proposed in (Margaris et al., 2024) exhibits very low

coverage, ranging from 1.7% to 15.11%, with the

coverage dropping between 74.1% and 83.5%, being

thus, again, practically non-applicable.

Comparable results are obtained when the number

of items offered per recommendation is increased to

5, in both cases. On the other hand, the algorithm

proposed in this paper retains the coverage achieved

by the plain CF algorithm in all cases.

5.2.2 Recommendation Accuracy

Considering that the algorithm in (Margaris et al.,

2024) has been shown in subsection 5.2.1 to be

practically not applicable for sparse datasets, due to

the sharp coverage drops, and additionally suffers

substantial coverage drops when applied to dense

datasets, in the following we will compare the

recommendation accuracy of the proposed algorithm

against the plain CF algorithm only.

Figure 3 depicts the recommendation precision of

the top-3 recommendations, when the KNN technique

is employed with K=200. Considering the mean of all

five datasets, the presented algorithm increases the

recommendation precision by 3% (from 82% to

84.5%). At individual dataset level, two cases are

notable: firstly, when the plain CF algorithm is used

with the MovieLens 1M dataset, the precision results

are mediocre (67.9%). However, when the presented

100

Amazon

Videogames

Amazon

Digital

Music

CiaoDVD MovieLens

100K

MovieLens

AVG

Coverage of recommendations

KNN, top-3 recommendations

K=200

plain CF proposed algorithm (Margaris et al., 2024)

WEBIST 2024 - 20th International Conference on Web Information Systems and Technologies

376

algorithm is employed, the precision is enhanced at

71.7% (a 5.6% increase). Secondly, when considering

the Amazon Digital Music dataset, the plain CF

algorithm achieves a recommendation precision

equal to 96%, leaving very small room for

enhancement. Nevertheless, the proposed algorithm

achieves to enhance the recommendation precision

even by a small amount, to 96.67%.

Figure 3: Mean precision value of the top-3

recommendations, under the KNN technique (K=200).

Figure 4 depicts the mean real rating value of the

top-3 recommendations, when the KNN technique is

employed (again, with K=200). Considering the mean

of all five datasets, the presented algorithm is able to

increase the recommendation value by 1.9% (from

4.27/5 to 4.35/5). The improvements observed for the

MovieLens 1M and Amazon Digital Music datasets

are similar to the ones discussed regarding the

recommendation precision: in MovieLens 1M a

considerable improvement is achieved (3.86/5 is

elevated to 3.98/5), while for the Amazon Digital

Music the improvement margins are very slim,

leading to modest gains (4.8/5 to 4.83/5).

Figure 4: Mean real rating value of the top-3

recommendations, when employing the KNN technique.

Figure 5 depicts the mean NDCG value of the top-

3 recommendations, when the KNN technique is

employed with K=200. Considering the mean of all

five datasets, the algorithm proposed in this paper

achieves a NDCG value enhancement from 0.973 to

0.979. The improvements observed for the

MovieLens 1M and Amazon Digital Music datasets

are similar to the ones discussed regarding the

previous two evaluation metrics: in MovieLens 1M a

considerable improvement is achieved (0.94 is

increased to 0.95), while for the Amazon Digital

Music the improvement margins are very slim,

leading to modest gains (0.991 to 0.993).

Figure 5: Mean NDCG value of the top-3

recommendations, when employing the KNN technique.

Similar results are observed when the

recommended items are increased to 5 (top-5

recommendations). More specifically, the mean

precision value is enhanced from 82.6% to 84.5%, the

mean rating value from 4.28/5 to 4.35/5 and the mean

NDCG from 0.967 to 0.973.

When K is increased to 500 (500 NNs per user),

similar results are observed: the average precision

value increases from 82.6% to 85.1%, the mean rating

value from 4.31/5 to 4.38/5 and the mean NDCG from

0.973 to 0.978, in the top-3 recommendations setting.

Regarding the top-5 recommendations setting, the

respective numbers are 83.2% and 85.2%, 4.31/5 and

4.38/5, and 0.967 and 0.973.

Regarding the mean recommendation precision of

the top-3 recommendations, when the THR technique

is employed, with the threshold being set to T=0.0,

considering the mean of all five datasets, the

presented algorithm increases recommendation

precision by 2.3% (from 85.2% to 87.2%). The

respective mean rating value of all five datasets, is

found increase by 1.6% (from 4.35/5 to 4.42/5), while

the mean NDCG value increases from 0.975 to 0.979.

Similar results are observed when the number of

items per recommendation are increased from 3 to 5

(top-5 recommendations). More specifically, the

mean precision is upgraded from 85.5% to 87%, the

average rating value is upgraded from 4.37/5 to

4.42/5, while the mean NDCG from 0.970 to 0.975.

When threshold T is increased to 0.5, similar

results are again observed. More specifically, the

mean precision value is enhanced from 84.8% to

86.6%, the mean rating value from 4.35/5 to 4.42/5

and, finally, the mean NDCG is enhanced from 0.975

to 0.979, in the top-3 recommendations case. When

the recommendations are increased from 3 to 5 (top-

5 recommendations), the respective numbers are 85%

and 86.4%, 4.36/5 and 4.41/5, and 0.970 and 0.975.

100

Amazon

Videogames

Amazon

Digital Music

CiaoDVD MovieLens

100K

MovieLens

AVG

AVG recommendations precision %

KNN, top-3 recommendations

K=200

plain CF proposed algorithm

3.6

3.8

4.2

4.4

4.6

4.8

Amazon

Videogames

Amazon

Digital Music

CiaoDVD MovieLens

100K

MovieLens

AVG

AVG real rating of the

recommendations

KNN, top-3 recommendations

K=200

top-3 plain CF top-3 proposed algorithm

0.94

0.95

0.96

0.97

0.98

0.99

Amazon

Videogames

Amazon

Digital Music

CiaoDVD MovieLens

100K

MovieLens

AVG

AVG Normalized DCG

KNN, top-3 recommendations

K=200

top-3 plain CF top-3 proposed algorithm

Improving Recommendation Quality in Collaborative Filtering by Including Prediction Conﬁdence Factors

377

5.2.3 Execution Efficiency

The presented algorithm introduces three distinct

overheads, in comparison to the plain CF algorithm.

The first one concerns the rating prediction step

where (a) the average rating value of each item and of

each user, and (b) the NN number of each rating

prediction, have to be calculated. Both of these

computations can be performed offline, as well as the

PCC metric, used in this work, includes the

calculation of the average rating value of each user,

and therefore this overhead is considered negligible.

The second overhead concerns the partitioning of

each rating prediction to the four subsets (ICRL

Top

ICRL

Med

, ICRL

Low

and “eliminated”), which takes

place in the step 2 of the proposed algorithm. Since

this process requires two minor additional actions

(one comparison and a separate store), for each rating

prediction, again, this individual overhead is

considered negligible.

The last overhead concerns the sorting of the

rating predictions within each subset. Since the plain

CF recommendation algorithm performs anyhow a

sorting of all rating predictions generated for each

user, there is no additional overhead. In fact, since the

algorithm needs to sort three smaller sets, rather than

one larger one, the proposed algorithm will need less

time to perform the sorting, as compared to the plain

CF algorithm.

As a result, based on the overhead analysis, the

overall additional overhead is considered negligible.

Furthermore, to verify the aforementioned theoretical

overhead analysis output, we measured the execution

times of two datasets, the Amazon Videogames and

the MovieLens 100K, between the plain CF and the

proposed algorithm. The additional overhead was

found to be less than 1.2% in both datasets.

6 CONCLUSIONS AND FUTURE

WORK

In this paper, we presented a CF recommendation

algorithm which determines the items to be

recommended by considering both the rating

prediction values and confidence factors of

predictions, allowing for predictions with higher

confidence factors to outrank predictions with higher

value, but lower confidence. The presented algorithm

achieves to enhance the recommendation quality,

while at the same time retaining the number of

recommendations for each user.

More specifically, the presented algorithm

partitions the items candidate for recommendation

into three subsets/groups, based on their rating

prediction values, corresponding to items where the

user will (i) “definitely” like them, (b) “most

probably” like them and (c) “probably” like them.

Afterwards, the algorithm sorts the items contained in

each set, in descending order, based not on their rating

prediction value (as the plain CF algorithm does), but

on the number of confidence factors the items’

predictions satisfy. At the end, the recommendation

process begins to select items from the first subset,

then continues, as needed, to the second and finally to

the third one, for each user.

The proposed algorithm was evaluated through a

set of experiments, which included five rating

datasets, both dense and sparse, as well as two NN

selection methods. These experiments have shown

that the proposed algorithm maintains

recommendation coverage levels, while achieving

satisfactory enhancement in recommendation

accuracy, as calculated in terms of (i)

recommendation precision, (ii) mean real rating value

of the recommended items, and (iii) NDCG metrics.

Furthermore, the presented algorithm (i) needs no

supplementary information concerning either the

users or the items, and (ii) has been shown to induce

negligible additional overhead, indicating both its

wide applicability and effectiveness.

Regarding future work, we are planning to

explore more features related to prediction accuracy

and apply them into the recommendation process.

Furthermore, we will focus on including basic

supplementary RecSys information sources, e.g. user

and item attributes, demographics, and types-

categories of items.

REFERENCES

Ajaegbu, C. (2021). An optimized item-based collaborative

filtering algorithm. Journal of Ambient Intelligence and

Humanized Computing, 12(12), 10629–10636.

https://doi.org/10.1007/s12652-020-02876-1

Alhijawi, B., Al-Naymat, G., Obeid, N., & Awajan, A.

(2021). Novel predictive model to improve the

accuracy of collaborative filtering recommender

systems. Information Systems, 96, 101670.

https://doi.org/10.1016/j.is.2020.101670

Bobadilla, J., Gutiérrez, A., Yera, R., & Martínez, L.

(2023). Creating synthetic datasets for collaborative

filtering recommender systems using generative

adversarial networks. Knowledge-Based Systems, 280,

111016. https://doi.org/10.1016/j.knosys.2023.111016

Chen, L., Yuan, Y., Yang, J., & Zahir, A. (2021). Improving

the Prediction Quality in Memory-Based Collaborative

Filtering Using Categorical Features. Electronics,

10(2), 214. https://doi.org/10.3390/

electronics10020214

Chen, Y.-C., Hui, L., & Thaipisutikul, T. (2021). A

collaborative filtering recommendation system with

WEBIST 2024 - 20th International Conference on Web Information Systems and Technologies

378

dynamic time decay. The J. of Supercomputing, 77(1),

244–262. https://doi.org/10.1007/s11227-020-03266-2

Chin, J. Y., Chen, Y., & Cong, G. (2022). The Datasets

Dilemma: How Much Do We Really Know About

Recommendation Datasets? Proceedings of the

Fifteenth ACM International Conference on Web

Search and Data Mining, 141–149.

https://doi.org/10.1145/3488560.3498519

Fareed, A., Hassan, S., Belhaouari, S. B., & Halim, Z.

(2023). A collaborative filtering recommendation

framework utilizing social networks. Machine Learning

with Applications, 14, 100495.

https://doi.org/10.1016/j.mlwa.2023.100495

Felfernig, A., Boratto, L., Stettinger, M., & Tkalčič, M.

(2018). Evaluating Group Recommender Systems. In

A. Felfernig, L. Boratto, M. Stettinger, & M. Tkalčič,

Group Recommender Systems (pp. 59–71). Springer.

https://doi.org/10.1007/978-3-319-75067-5_3

Fkih, F. (2022). Similarity measures for Collaborative

Filtering-based Recommender Systems: Review and

experimental comparison. Journal of King Saud

University – Comp. and Inf. Sciences, 34(9), 7645–

7669. https://doi.org/10.1016/j.jksuci.2021.09.014

Guo, G., Zhang, J., Thalmann, D., & Yorke-Smith, N.

(2014). ETAF: An extended trust antecedents

framework for trust prediction. 2014 IEEE/ACM

International Conference on Advances in Social

Networks Analysis and Mining, 540–547.

https://doi.org/10.1109/ASONAM.2014.6921639

Harper, F. M., & Konstan, J. A. (2016). The MovieLens

Datasets: History and Context. ACM Transactions on

Interactive Intelligent Systems, 5(4), 1–19.

https://doi.org/10.1145/2827872

Jain, G., Mahara, T., & Sharma, S. C. (2023). Performance

Evaluation of Time-based Recommendation System in

Collaborative Filtering Technique. Procedia Computer

Science, 218, 1834–1844. https://doi.org/10.1016/j.

procs.2023.01.161

Krichene, W., & Rendle, S. (2020). On Sampled Metrics

for Item Recommendation. Proceedings of the 26th

ACM SIGKDD International Conference on

Knowledge Discovery & Data Mining, 1748–1757.

https://doi.org/10.1145/3394486.3403226

Li, D., Jin, R., Gao, J., & Liu, Z. (2020). On Sampling Top-

K Recommendation Evaluation. Proceedings of the

26th ACM SIGKDD International Conference on

Knowledge Discovery & Data Mining, 2114–2124.

https://doi.org/10.1145/3394486.3403262

Margaris, D., Sgardelis, K., Spiliotopoulos, D., &

Vassilakis, C. (2024). Exploiting Rating Prediction

Certainty for Recommendation Formulation in

Collaborative Filtering. Big Data and Cognitive

Computing, 8(6), 53. https://doi.org/10.3390/

bdcc8060053

Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2020).

What makes a review a reliable rating in recommender

systems? Inf. Processing & Management, 57(6),

102304. https://doi.org/10.1016/j.ipm.2020.102304

Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2022).

On Producing Accurate Rating Predictions in Sparse

Collaborative Filtering Datasets. Information, 13(6),

302. https://doi.org/10.3390/info13060302

Nguyen, L. V., Vo, Q.-T., & Nguyen, T.-H. (2023).

Adaptive KNN-Based Extended Collaborative Filtering

Recommendation Services. Big Data and Cognitive

Computing, 7(2), 106. https://doi.org/10.3390/

bdcc7020106

Ni, J., Li, J., & McAuley, J. (2019). Justifying

Recommendations using Distantly-Labeled Reviews

and Fine-Grained Aspects. Proceedings of the 2019

Conference on Empirical Methods in Natural Language

Processing and the 9th International Joint Conference

on Natural Language Processing (EMNLP-IJCNLP),

188–197. https://doi.org/10.18653/v1/D19-1018

Sen, R., Goswami, S., & Chakraborty, B. (2019). Jeffries-

Matusita distance as a tool for feature selection. 2019

International Conference on Data Science and

Engineering (ICDSE), 15–20. https://doi.org/10.1109/

ICDSE47409.2019.8971800

Singh, P. K., Sinha, M., Das, S., & Choudhury, P. (2020).

Enhancing recommendation accuracy of item-based

collaborative filtering using Bhattacharyya coefficient

and most similar item. Applied Intel., 50(12), 4708–

4731. https://doi.org/10.1007/s10489-020-01775-4

Spiliotopoulos, D., Margaris, D., & Vassilakis, C. (2022).

On Exploiting Rating Prediction Accuracy Features in

Dense Collaborative Filtering Datasets. Information,

13(9), 428. https://doi.org/10.3390/info13090428

Trattner, C., Said, A., Boratto, L., & Felfernig, A. (2024).

Evaluating Group Recommender Systems. In A.

Felfernig, L. Boratto, M. Stettinger, & M. Tkalčič

(Eds.), Group Recommender Systems (pp. 63–75).

Springer Nature Switzerland. https://doi.org/10.1007/

978-3-031-44943-7_3

Vuong Nguyen, L., Nguyen, T., Jung, J. J., & Camacho, D.

(2021). Extending collaborative filtering

recommendation using word embedding: A hybrid

approach. Concurrency and Computation: Practice and

Experience, 35(16), e6232. https://doi.org/10.1002/

cpe.6232

Wang, D., Yih, Y., & Ventresca, M. (2020). Improving

neighbor-based collaborative filtering by using a hybrid

similarity measurement. Exp. Syst. with Appl., 160,

113651. https://doi.org/10.1016/j.eswa.2020.113651

Wang, R., Wu, Z., Lou, J., & Jiang, Y. (2022). Attention-

based dynamic user modeling and Deep Collaborative

filtering recommendation. Expert Systems with

Applications, 188, 116036. https://doi.org/10.1016/j.

eswa.2021.116036

Wang, Z. (2023). Intelligent recommendation model of

tourist places based on collaborative filtering and user

preferences. Applied Artificial Intel., 37(1), 2203574.

https://doi.org/10.1080/08839514.2023.2203574

Zhang, L., Li, Z., & Sun, X. (2021). Iterative rating

prediction for neighborhood-based collaborative

filtering. Applied Intelligence, 51(10), 6810–6822.

https://doi.org/10.1007/s10489-021-02237-1

Improving Recommendation Quality in Collaborative Filtering by Including Prediction Conﬁdence Factors

379