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Implicit social recommendation algorithm based on multilayer fuzzy perception similarity
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DOI：10.1007/s13042-021-01409-2.png)
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Most recommender systems are essentially a contextually accurate matching between users and items with similarities. Thus, similarity is particularly important to the recommendation process. Furthermore, the highest goal of similarity is to simulate the subjective human feeling of similarity, i.e., to simulate objective feature engineering in a way that is as consistent with subjective feeling as possible. By studying the subjective cognition of similarity, we found that the process could be divided into two stages, namely, perception and comprehension. Perception has fuzziness in that deterministic data cannot accurately describe subjective perception and judge emotional tendencies. Second, comprehension has gradations such that a linear model easily underfits the similarity. To address these two problems, we proposed a new implicit social recommendation algorithm based on multilayer fuzzy perception similarity. An extensive experimental study conducted on benchmark datasets showed that the proposed algorithm is very competitive with some of the traditional recommendation algorithms and state-of-the-art neural network algorithms, especially in terms of the obtained rankings.
Keyword:
Fuzzy modeling
Composite similarity
Subjective perception
Feature engineering
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