Improving Collaborative Filtering’s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors

Margaris, Dionisis; Vasilopoulos, Dionysios; Vassilakis, Costas; Spiliotopoulos, Dimitris

doi:10.1109/iisa.2019.8900678

Cited by 8 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we compared the ITRA model with other advanced approaches, namely, UCF, TAR, FTM, DLM and STPMF. In addition, compared to the prediction accuracy of previous synthetic algorithms applied in recommendation systems [45], [47], [55], the proposed model in our article is much more suitable for recommendations in terms of accuracy. That is to say, by mining user implicit trust, more efficient rating predictions can be achieved.…”

Section: Discussionmentioning

confidence: 89%

“…To have a better understanding of the prediction accuracy, our article conducts comparative experiments to compare the prediction accuracy of our ITRA model with those of other advanced algorithms on three datasets. In addition, to compare the experimental accuracy, the target user's nearest neighbors (NN) can be set from small to large: 5,15,25,35,45,55,65,75, 85 and 95. The prediction results are shown as follows.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Liu

Ren

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information. However, most existing collaborative recommendation models suffer from poor accuracy due to data sparsity and cold start problems that recommender systems contain only a few explicit data. To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems. Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network. Then, the trust ratings mined from user trust neighbors are used to compute trust similarity among users based on user collaborative filtering model. Finally, using the above filtered trust ratings and user trust similarity, the prediction results are generated by a trust weighting method. In addition, the empirical experiments are conducted on three real-world datasets, and the results demonstrate that our rating prediction model has obvious advantages over the state-of-the-art comparison methods in terms of the accuracy of recommendations.

show abstract

Section: Discussionmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Liu

Ren

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The CF negNNs algorithm presented in [26] increases the CF rating prediction coverage in sparse datasets, by incorporating, in the rating prediction computation process, users with negative similarity to the user for whom the rating prediction is computed. Margaris et al [27] propose the CF VNN algorithm, which creates virtual user profiles, termed virtual NNs (VNNs), by merging pairs of NN profiles corresponding to real users having high similarity. The introduction of the VNN profiles contributes to the alleviation of the "grey sheep" problem.…”

Section: Related Workmentioning

confidence: 99%

“…The CF VNN algorithm [27], where artificial user profiles (virtual near neighbours-VNNs) are created by combining pairs of real users which are NNs of high similarity. The CF VNN algorithm was configured to use the optimal parameters for its operation reported in [27], i.e., Th(sim) = 1.0 and Th(cr) = 1. For more details on the parameters, the interested reader is referred to [27].…”

Section: Prediction Coverage Increasementioning

confidence: 99%

“…Finally, it is worth noting that the CF DR algorithm, proposed in this paper, can be combined with other works in the domain of CF, targeting either to (1) enhance rating prediction accuracy [14][15][16][17], (2) improve recommendation quality [18][19][20][21], (3) increase rating prediction computation efficiency [22][23][24][25], or (4) further increase prediction coverage in CF-based RSs [26][27][28][29].…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

et al. 2020

Self Cite

View full text Add to dashboard Cite

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

show abstract

Two new collaborative filtering approaches to solve the sparsity problem

Koohi

Kiani

2020

Cluster Comput

View full text Add to dashboard Cite

Improving Collaborative Filtering’s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors

Cited by 8 publications

References 31 publications

A Novel Implicit Trust Recommendation Approach for Rating Prediction

A Novel Implicit Trust Recommendation Approach for Rating Prediction

An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

Two new collaborative filtering approaches to solve the sparsity problem

Contact Info

Product

Resources

About