An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem
Abstract
:1. Introduction
1.1. Motivation
1.2. Paper Contribution
- A single asymmetric similarity method has been proposed by taking account of items’ genre with the reliability between them which defines the direct asymmetric similar relation of items.
- Another new similarity method has been defined by identifying the correlations of items based on the transitive relations of reliability between them.
- A prediction algorithm is proposed to increase the accuracy of recommendation.
- A detailed experiment is done to prove our statement that the proposed methodology outperforms existing methods.
2. Related Works
3. Proposed Method
3.1. Genre Based Correlation Determination
3.2. Confidence with Laplace Correction
3.3. Direct Inter-Connectivity Detection: Genre Based Item–Item Asymmetric Similarity
3.4. Relative Inter-Connectivity Detection: Inferring Transitivity of Reliable Correlation of Items
3.5. Proposed Prediction Algorithm
Algorithm 1: Enhanced Prediction Algorithm |
Input: A list of users, and items, and similarity values of items, Output: A list of predicted ratings
|
4. Experimental Setup and Evaluations
4.1. Dataset
4.2. Evolution Metrics
4.2.1. Mean Absolute Error
4.2.2. Precision
4.2.3. Recall
4.2.4. F-Measures
4.2.5. Mean Reciprocal Rank
5. Results and Discussion
5.1. Prediction Accuracy
5.1.1. Performance Evaluation: Movielens DataSet
5.1.2. Performance Evaluation: MovieTweets DataSet
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Movie/Genre | Action | Romantic | Horror | Adventure |
---|---|---|---|---|
Stardust | - | 1 | - | 1 |
Mr. and Mrs. Smith | 1 | 1 | - | - |
Wolf Girl | - | 1 | 1 | 1 |
Constantine | 1 | - | 1 | - |
Wrong Turn | - | - | 1 | - |
Stardust | Mr. and Mrs. Smith | Wolf Girl | Constantine | Wrong Turn | |
---|---|---|---|---|---|
Stardust | - | 0.250 | 0.165 | 0.000 | 0.000 |
Mr. and Mrs. Smith | - | - | 0.165 | 0.250 | 0.000 |
Wolf Girl | - | - | - | 0.165 | 0.330 |
Constantine | - | - | - | - | 0.500 |
Item1 | Item2 | Item3 | Item4 | Item5 | |
---|---|---|---|---|---|
Item1 | - | 0.52 | 0.00 | 0.33 | 0.25 |
Item2 | 0.40 | - | 0.65 | 0.00 | 0.00 |
Item3 | 0.00 | 0.25 | - | 0.16 | 0.33 |
Item4 | 0.81 | 0.00 | 0.30 | - | 0.50 |
Item5 | 0.10 | 0.00 | 0.41 | 0.65 | - |
Item1 | Item2 | Item3 | Item4 | Item5 | |
---|---|---|---|---|---|
Item1 | - | 0.52 | 0.41 | 0.33 | 0.25 |
Item2 | 0.40 | - | 0.65 | 0.38 | 0.41 |
Item3 | 0.35 | 0.25 | - | 0.16 | 0.33 |
Item4 | 0.81 | 0.66 | 0.30 | - | 0.50 |
Item5 | 0.10 | 0.31 | 0.41 | 0.65 | - |
Datasets | Users | Items | Rating Range | Genre |
---|---|---|---|---|
Movielens | 6040 | 3952 | 1–5 | 19 |
MovieTweets | 43,357 | 25,193 | 1–10 | 28+ |
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Hasan, M.; Roy, F. An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem. Big Data Cogn. Comput. 2019, 3, 39. https://doi.org/10.3390/bdcc3030039
Hasan M, Roy F. An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem. Big Data and Cognitive Computing. 2019; 3(3):39. https://doi.org/10.3390/bdcc3030039
Chicago/Turabian StyleHasan, Mahamudul, and Falguni Roy. 2019. "An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem" Big Data and Cognitive Computing 3, no. 3: 39. https://doi.org/10.3390/bdcc3030039