Aim and Objective:
Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the
feature metric hidden in the sequence. Otherwise, the graphical representation of biologic sequence is an important tool for
sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences.
Materials and Methods:
The transition probability is used to describe amino acid combinations of protein sequences. The
combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition
probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from transition probability graph to transition probability vector by k-order transition probability graph. Transition
entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is
applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.
Results:
By constructing a phylogenetic tree, we find that the results of each application are consistent with other studies.
Conclusion:
The graphical representation proposed in this article is a practical and correct method.
E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.
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