A collaborative filtering recommendation system with dynamic time decay

Chen, Yi Cheng; Lin, Hui; Thaipisutikul, Tipajin

doi:10.1007/s11227-020-03266-2

Cited by 44 publications

(25 citation statements)

References 27 publications

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“…It works the same as regular recommendation systems but differs from it by looking for topics of interest to the user instead of items. We also reviewed the literature on heterogeneous information networks [7,[19][20][21] which is the nature of the social network, recommendation systems [22][23][24], and personality computing [25][26][27].…”

Section: Related Studiesmentioning

confidence: 99%

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

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Section: Related Studiesmentioning

confidence: 99%

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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“…In recent years, many scholars had conducted more in-depth studies on the influence of time on the recommendation. Gao et al [15] combined the time weighting of user rating time and the LDA clustering of item type to obtain the predicted score; Zhang et al [16] constructed a dense scoring matrix and proposed the concept of the time window, integrating time weighting into the rating matrix; Chen et al [17] extended the concept of human memory by introducing dynamic time decay into changes in user preferences. Although they took into account the impact of time, they ignored the influence of users' social relations and failed to conduct a detailed analysis of users' data resources in different periods.…”

Section: Related Workmentioning

confidence: 99%

“…A CF recommendation system Fig. 6 Recommendation effect corresponding to different numbers of rating data with dynamic time decay (DDCF) [17], CF recommendation algorithm integrating time windows and rating predictions (TPMF-CF) [16], Time LDA Combinational Algorithm(TLCA) [15], and the traditional collaborative filtering recommendation (CF) algorithm are chosen. The contents of these algorithms are briefly explained below:…”

Section: Algorithm Comparisonmentioning

confidence: 99%

“…Especially, from the experimental results, we can see that the evaluation factors of our algorithm are better than DDCF, but the results of the two algorithms are very close. That's because that they are both based on the concept of human brain memory to adjust the attenuation function through the user's behavior [17]. At the same time, the user's preference level is specified, which has some similarities with the short-term interest evolution in our algorithm.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

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Collaborative filtering algorithm with social information and dynamic time windows

Wang

et al. 2021

Appl Intell

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With the rapid development of social networks, the problem of information overload is increasingly serious. The recommendation system can deal with the problem of information overload effectively and provide users with personalized recommendation services. In the process of recommendation, the traditional recommendation algorithms do not take the social relationship of users as the basis of recommendation; at the same time, they do not take for the dynamic change of user's interest and think that it is immutable. About these problems, the paper proposes a personalized recommendation algorithm with social information and dynamic time windows. Firstly, a collaborative filtering algorithm is proposed which integrates social information and user interest in the process of searching the nearest neighbor. Secondly, the time windows are dynamically adjusted to obtain a stable increment and better reflect the short-term interests of users. Then, the concept of time function is introduced to allocate corresponding time weights for users' interests in different periods. Finally, we conduct a series of experiments to verify the practicability and effectiveness of our algorithm. Experimental results show that the performance of the proposed algorithm is better than the traditional collaborative filtering recommendation algorithm.

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“…The core of a personalized recommendation system is the recommendation algorithm, which mainly includes the content-based recommendation algorithm, collaborative filtering recommendation algorithm, and hybrid recommendation algorithm [3,4]. Among them, because of the high efficiency, accuracy, and personalization, the collaborative filtering recommendation algorithm has become one of the most effective and extensive application recommendation algorithms [5].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Cai

Tang

et al. 2021

Applied Sciences

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The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

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A collaborative filtering recommendation system with dynamic time decay

Cited by 44 publications

References 27 publications

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

Collaborative filtering algorithm with social information and dynamic time windows

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

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