Asymmetric response aggregation heuristics for rating prediction and recommendation

Ji, Shujuan; Wei, Yang; Guo, Shenghui; Chiu, Dickson K.W.; Zhang, Chunjin; Yuan, Xinyue

doi:10.1007/s10489-019-01594-2

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…Second, we use the second method proposed in section 3.3.1 to obtain neighbors, and use the first method proposed in section 3.3.2 to give the neighbors an initial weight. Using the attribute information of the target item to obtain its neighbor items, the target user lacks the attribute information, so the neighbor users of the target user are obtained according to the method of part (2) of section 3.3.1; The initial weight of the neighbor item is given according to the attribute similarity of the item, and the initial user is given an initial weight according to (9). The experimental selection recommendation list is Top-5, and the abscissa is the similarity threshold .…”

Section: Resultsmentioning

confidence: 99%

“…We use the second method proposed in section 3.3.1 to obtain neighbors, and use the second method proposed in section 3.3.2 to give the neighbors an initial weight. We give the neighboring item an initial weight according to the importance of the item node mentioned in part (2) of section 3.3.2 and give the neighboring users an initial weight according to (9). The abscissa is the length of the tag recommendation list, and the ordinate is the F1 value.…”

Section: Resultsmentioning

confidence: 99%

“…With the vigorous development of machine learning, more and more algorithms are playing their role in big data processing [4][5][6]. Among them, the recommendation system has achieved great success in information filtering [7][8][9] and tag recommendation can be used to describe the user's preference and item's characteristics more efficiently. In the tag system, the user can freely add a description to the online item according to personal cognition, generally in the form of a phrase or keyword data.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

Zhao¹,

Zhang²,

Sun³

et al. 2021

KSII TIIS

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The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

Zhao¹,

Zhang²,

Sun³

et al. 2021

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…However, few existing models exhibit this characteristic. ey assume that the rumor propagation rate is a constant throughout the whole propagation process, which ignores the potential influence of ignorant's neighbors [24] on the ignorant when the ignorant turns into a spreader. ere is also such a social phenomenon, that is, the attitude of individuals towards rumor would change due to the perception from life.…”

Section: Introductionmentioning

confidence: 99%

C-SIW Rumor Propagation Model with Variable Propagation Rate and Perception Mechanism in Social Networks

Qiu

Liu

2020

Discrete Dynamics in Nature and Society

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The propagation of rumor has become a common phenomenon in social networks. Studying the dynamic propagation of rumor can help locate the key points to control rumor propagation. To further research the internal motivation of state transition, a corrector-ignorant-spreader-weakener (C-SIW) model is proposed in this paper. When the individual changes state to transmit rumor, the neighbor may have a significant impact on rumor propagation. Considering the point, this paper constructs a function to describe the propagation rate, which relates to the state of neighbors and the reputation of the spreader. In addition, perception from life also can cause individual state changes. Based on the above fact, the links from the spreader and the weakener to the corrector are added to describe the perception mechanism. Then, combining the derived average field equations, the steady state of the model is analyzed and verified in experimental simulation. Moreover, the experimental results on different networks show that the perception mechanism reduces the rumor influence. Besides, the variable propagation rate can position the fast-growing stage of rumor propagation more accurately and facilitate the control of rumor propagation.

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“…In this case, more people, especially leader spreaders with strong influence on individuals, understand the original meaning of the matter, which can control the disturbance to the public order. Hence, combining with different circumstances in real life, 3‐5 the establishment of appropriate propagation model can provide relevant managers with evidence for working out methods to control information propagation.…”

Section: Introductionmentioning

confidence: 99%

Spectator‐filter‐spreader‐stifler information propagation model with wake‐up mechanism in social networks

Liu

Sun

Qiu

et al. 2020

Concurrency and Computation

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Summary Nowadays, social network platforms play a significant role in daily life, allowing people to hear what is going on around the world without leaving home. The research on the internal transformation of information propagation is critical, which can help restrain the propagation of fake information or rumors and promote the widespread dissemination of effective information. To better understand the transition of states in information propagation, this article considers a new filter state that does not transmit information temporarily and establishes a spectator‐filter‐spreader‐stifler (SFIR) model. Mainly, to explore the dynamics of information propagation, the article introduces two different wake‐up mechanisms including the spontaneous awakening of the filter and the infection of the leader spreaders to the passive stifler, which relies on the proportion of current spreaders as well as the doubt degree on information. Afterward, based on the average field equations built, the nonpropagation equilibrium point E0 and propagation equilibrium point E1 are obtained by theoretical analysis. Moreover, the simulation results reveal that spontaneous wake‐up mechanism accelerates the information propagation while leader spreaders infection increases the scope of information propagation, which can provide theoretical support for controlling information propagation more effectively.

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Asymmetric response aggregation heuristics for rating prediction and recommendation

Cited by 12 publications

References 36 publications

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

C-SIW Rumor Propagation Model with Variable Propagation Rate and Perception Mechanism in Social Networks

Spectator‐filter‐spreader‐stifler information propagation model with wake‐up mechanism in social networks

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