.png){kind=icon}
.png){kind=icon}
Return
Optimized recommendations by user profiling using apriori algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2021.107272.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Collaborative filtering has been the most straightforward and most preferable approach in the recommender systems. This technique recommends an item to a target user from the preferences of top-k similar neighbors. In a sparse data scenario, the recommendation accuracy of the collaborative filtering degrades significantly due to the limitations of existing various similarity measures. Such constraints offer an open scope for enhancing the accuracy of optimized user-specific recommendations. Many techniques have been utilized for this, like Particle Swarm Optimization and other evolutionary collaborative filtering algorithms. The proposed approach utilizes the Apriori algorithm to form users' profiles from the items' ratings and categorical attributes. The user profile creation is performed using the apriori algorithm. The profile of each user involves the likes and disliking of categorical characteristics of objects by users. In the collected MovieLens dataset, the efficiency of the proposed recommendation approach is tested. The comparative results show proof that the proposed novel algorithm outperforms other prominent collaborative filtering algorithms on the MovieLens datasets based on rating prediction accuracy. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Optimized recommendation
Collaborative filtering
Similarity measure
Items' categorical attribute
Users' liking attribute
Users' disliking attributes
User's profile
Top-N recommendation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
View more organizations