Recommendation algorithm based on user score probability and project type

Wu, Chunxue; Wu, Jing; Luo, Chong; Wu, Qiao; Liu, Cong; Wu, Yan; Yang, Fan

doi:10.1186/s13638-019-1385-5

Cited by 19 publications

(31 citation statements)

References 26 publications

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“…To adopt this user behavior, Wu et al [29] developed a new similarity algorithm that involves not only explicit rating data but also user behavior data in giving an implicit rating. Wu et al [29] assumed that users who gave a low rating to an item did not necessarily dislike the item. The similarity algorithm combines similarity…”

Section: Similarity Algorithmmentioning

confidence: 99%

“…P u1 and P u2 are the average probability scores for all item types from users u 1 and users u 2 , respectively, and g is one type of items rated by both users. The combination of the two similarities in the research conducted by Wu et al [29] has several limitations, viz the calculation of similarity based on user behavior value only considers the genre data of the item so that it does not guarantee the resulted recommendations accuracy. Based on the problem, this paper focuses on increasing accuracy by considering other user behavior data that is the user profile data (namely age, gender, occupation, and location) that will influence user behavior in determining the selected item.…”

Section: Similarity Algorithmmentioning

confidence: 99%

“…The four similarity algorithms that have been developed by previous researchers perform similarity calculations based only on the user's rating value data. Furthermore, Wu et al [29] proposed a new similarity by combining similarity based on the user's rating value and similarity based on user behavior value. Wu et al [29] view that the traditional recommendation algorithm only pays attention to the rating value given by users explicitly, but ignores implicit information on user behavior in assessing the item type (genre) that will also affect the accuracy level.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Wu et al [29] proposed a new similarity by combining similarity based on the user's rating value and similarity based on user behavior value. Wu et al [29] view that the traditional recommendation algorithm only pays attention to the rating value given by users explicitly, but ignores implicit information on user behavior in assessing the item type (genre) that will also affect the accuracy level. In their research, similarity based on the user's rating value (S r ) was calculated using the UBCF method.…”

Section: Introductionmentioning

confidence: 99%

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User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Widiyaningtyas

Hidayah

Adji

2020

Preprint

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A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

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Section: Similarity Algorithmmentioning

confidence: 99%

Section: Similarity Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Widiyaningtyas

Hidayah

Adji

2020

Preprint

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“…R ECOMMENDER Systems (RSs) have received increasing attention in recent years. The systems have critical roles for most websites and e-commerce sites, such as online dating [1], movie recommendations [2]- [6], the evaluation of temporal networks [7], [8], collaborative recommendations [9] and so on. Technology titans like Amazon, Netflix and Taobao are using similar systems and algorithms to find new customers as well as selling more merchandise to old ones.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Collaborative Filtering Algorithms Based on Complex Network Modeling and Degree Centrality

Wang

et al. 2020

IEEE Access

Self Cite

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Given that everyone online is saturated with information, the theoretical significance of recommendation algorithms is evident in the fact that users need help finding products and content they care about. Collaborative filtering predicts a user's rating on an item by finding similar users that rated the item or similar items that were rated by the user, and using the selected similar neighbors to "collaboratively filter" the recommendation. In the process, selected neighbors are considered equally important despite their differences in popularity. Here, we explore a method of modeling recommender systems as networks that can be constructed by considering items as nodes and similarity between them as links. Our research shows that item centrality has a negative impact on the accuracy of rating predictions, which needs to be considered for better algorithm performance. Experiments show that collaborative filtering algorithms can be decentralized by our method and provide a better accuracy of rating prediction. Furthermore, the relationship between the prediction target and its neighbors can be further evaluated based on both their similarity and their centrality. INDEX TERMS recommender systems, complex networks, decentralized collaborative filtering, degree centrality, network modeling.

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CF-PMSS: Collaborative Filtering Based on Preference Model and Sparrow Search

Song

2023

Lecture Notes in Computer Science

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Recommendation algorithm based on user score probability and project type

Cited by 19 publications

References 26 publications

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Decentralized Collaborative Filtering Algorithms Based on Complex Network Modeling and Degree Centrality

CF-PMSS: Collaborative Filtering Based on Preference Model and Sparrow Search

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