Recommendation algorithm combining ratings and comments

Fang, Xujian; Wang, Jiayi; Seng, Dewen; Li, Binquan; Lai, Chenxuan; Chen, Xiyuan

doi:10.1016/j.aej.2021.04.056

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…Additionally, they encounter difficulties when attempting to capture intricate nonlinear relationships and higher-order feature interactions. In response to these limitations, some scholars have put forth factorization machine (FM) techniques tailored for high-dimensional feature spaces [8,27]. Moreover, there have been endeavors to develop hybrid models that harness the strengths of multiple recommendation techniques [26,28].…”

Section: Traditional Poi Recommendation Methodsmentioning

confidence: 99%

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

Li,

Yang,

Gong

et al. 2023

Electronics

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With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.

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Section: Traditional Poi Recommendation Methodsmentioning

confidence: 99%

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

Li,

Yang,

Gong

et al. 2023

Electronics

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“…1. Assuming that the conditional distribution of the known scoring data obeys the Gaussian distribution (Yang et al 2019;Fang et al 2020). It is possible to define the conditional distribution of the observed ratings as shown in formula 1.…”

Section: Probability Matrix Factorization Model (Pmf Model)mentioning

confidence: 99%

“…Although the collaborative filtering algorithm can analyze users' preference information and recommend favorite items for them, there is a problem of low prediction accuracy caused by the sparse user rating matrix (Wang 2020). Later, the potential factor model based on matrix factorization was successfully used in collaborative filtering, it effectively solves the problem of sparse scoring matrix by analyzing the potential characteristics between users and items, and its prediction accuracy and stability have been widely recognized (Fang et al 2020;Han et al 2020;Zhang ang Yang 2021).…”

mentioning

confidence: 99%

An improved constrained Bayesian probabilistic matrix factorization algorithm

Wang

Chen

et al. 2023

Soft Comput

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Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user's feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment prove that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users.

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“…Therefore, it is necessary to choose different recommendation mechanisms for various user feedback behaviors. Modeling the situation is critical [10]. Some researchers also provide a logical StyMarkov ensemble model, which can be constructed as a Markov chain with singers, and learn to use the vector representation of songs to predict the singers.…”

Section: Introductionmentioning

confidence: 99%

E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering

2023

ferm

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With the continued expansion of the EC scale, personalized recommendation technology is widely used. However, traditional referral systems cannot meet current data processing needs, and the presence of highly powerful big data analytics capabilities is a fundamental prerequisite for new personalized referral systems. The paper focuses primarily on EC recommended algorithm research based on big data analysis and fuzzy clustering gene analysis. Based on the literature data, understand the basic theoretical issues related to EC boosting calculations and analyze the methods of genetic fuzzy group analysis. The EC promotion algorithm is designed and the designed algorithm is tested. In conclusion, the algorithm given in this work has a low MAE like the other two algorithms, so its configuration quality is high.

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Recommendation algorithm combining ratings and comments

Cited by 13 publications

References 10 publications

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

An improved constrained Bayesian probabilistic matrix factorization algorithm

E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering

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