| Autor: |
Choudhary, Priyankar, Kant, Vibhor, Dwivedi, Pragya |
| Předmět: |
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| Zdroj: |
Procedia Computer Science; 2017, Vol. 115, p853-862, 10p |
| Abstrakt: |
Multi criteria recommender systems generate quality recommendations to users by incorporating criteria ratings into recommender system using collaborative filtering because ratings over multiple criteria can capture user preferences efficiently. However, aggregation of similarities computed on multiple criteria is still a major concern. Moreover, the concept of natural noise is an emerging trend that is related to inconsistent behaviour of users. Our work in this paper is an attempt towards developing multi criteria recommender systems that deals with inconsistent ratings and uses particle swarm optimization to learn optimal weights for a user over different criteria in the aggregation process. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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