An Improved Personalized Collaborative Filtering Recommendation Based on Items Diversity
How to Cite?
Masoud Ghorbanian, Noor Hafizah Binti Hassan, Mohd Shahidan Bin Abdullah, "An Improved Personalized Collaborative Filtering Recommendation Based on Items Diversity," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 13-20, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P203
With the rapid information growth and the flood of data, users usually face a huge number of items. Therefore, finding desirable items is a challenge for users. On the other hand, users’ behavior is changing, and these changes are creating pressure on the profits of the popular items, while at the same time, the new or previously unknown items are being ignored due to lack of coverage in recommendations. In other words, the existing algorithms have not fully covered the diversity and coverage of the items in the recommendation lists. In this paper, a personalized recommendation algorithm is proposed to improve the diversity and coverage of the items. The contributions are as follows: a method is proposed to calculate the user’s diversity level in order to improve items` diversity and coverage in recommendations by performing a collaborative filtering method and k-means cluster based on genre. Also, intra-list diversity, coverage, and accuracy results are investigated between the proposed recommendation algorithm and other similar recommendation algorithms. The results of the experiment show that our proposed recommendation algorithm improves the diversity and coverage of the items significantly while still preserving the users’ interest.
Accuracy, Collaborative filtering, Coverage, Diversity, the Recommendation algorithm
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