Predicting online ratings based on the opinion spreading process

He, Xingsheng; Zhou, Mingyang; Zhao, Zhuo; Fu, Zhongqian; Liu, Jianguo

doi:10.1016/j.physa.2015.05.066

Cited by 24 publications

(17 citation statements)

References 21 publications

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“…He et al in their work [2] proposed a method in 2015 based on similarity and top k similar neighbors, assuming opinion spread among users in recommender systems. The method provides more accurate predictions compared to some existing literature [2], [11]. This approach is considered as a memory-based CF, because the core idea is based on a similarity equation measuring the opinion spread between users.…”

Section: B Related Collaborative Filtering Methodsmentioning

confidence: 99%

“…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%

“…Similarity is usually calculated between each pair of users or items in this type of CF. And topk neighbors with the highest similarity are selected for the target user u [2]. However, some literature uses the k Nearest Neighbor (kNN) algorithm to find the most similar neighbors for the target user [10], [23].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2020

IEEE Access

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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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Section: B Related Collaborative Filtering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2020

IEEE Access

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“…Also, in social contexts, two persons would build friendship in the near future with a high probability if they have many common friends or attributes, which could be utilized to uncover lost friends or predict future friends 9 – 11 . Besides, further extensive applications also include personalized recommendations in e-commerce 12 , 13 and aircraft route planning study 14 , etc.…”

Section: Introductionmentioning

confidence: 99%

Link Prediction in Evolving Networks Based on Popularity of Nodes

Wang

Zhou

et al. 2017

Sci Rep

Self Cite

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Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

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“…First, given a rating scale where the highest score denotes the most positive opinion and the lowest score indicates the most negative opinion, users' rating scores do not distribute evenly along the whole rating scale [11]. Second, different users may have different rating criterions, some goodtempered users are willing to give high scores whereas other critical people seldom give full marks to any items they have watched [12]. Last but not least, the negative ratings indicate 2 Mobile Information Systems dislike and simultaneously relevance, and they may play an either negative or positive role depending on the sparsity of training set and the popularity of the corresponding items [13].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

Li¹,

Dong²,

Fu³

2017

Mobile Information Systems

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As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

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Predicting online ratings based on the opinion spreading process

Cited by 24 publications

References 21 publications

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

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

Link Prediction in Evolving Networks Based on Popularity of Nodes

Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

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