Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Bai, Yun; Jia, Suling; Wang, Shuangzhe; Tan, Binkai

doi:10.20944/preprints202002.0367.v1

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…There are many worthy research topics in network science including but not limiting to community detection [16], fractal dimension [17,18], link prediction [19], evolutionary game theory [20][21][22], self similarity analysis [23] and so forth. Algorithms and tools in network science can also be used for time series analysis [24,25], pattern recognition [26,27], multi-criteria decision making [28,29], uncertainty modeling [30], recommender system [31,32], just to name a few. We will see the emerging progress in both the theory and the applications of network science in the near future.…”

Section: Introductionmentioning

confidence: 99%

A modified gravity model based on network efficiency for vital nodes identification in complex networks

Li¹,

Shang²,

Deng³

2021

Preprint

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Vital nodes identification is an essential problem in network science. Various methods have been proposedto solve this problem. In particular, based on the gravity model, a series of improved gravity models are proposed to find vital nodes better in complex networks. However, they still have the room to be improved.In this paper, a novel and improved gravity model, which is named network efficiency gravity centrality model (NEG), integrates gravity model and network efficiency is proposed. Compared to other methods based on different gravity models, the proposed method considers the effect of the nodes on structure robustness of the network better. To solidate the superiority of the proposed method, experiments on varieties of real-world networks are carried out.

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Section: Introductionmentioning

confidence: 99%

A modified gravity model based on network efficiency for vital nodes identification in complex networks

Li¹,

Shang²,

Deng³

2021

Preprint

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A Map-Based Recommendation System and House Price Prediction Model for Real Estate

Mubarak

Tahir

Waqar³

et al. 2022

IJGI

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In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.

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Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

Wang

Cai

2020

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With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently.

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Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Cited by 4 publications

References 22 publications

A modified gravity model based on network efficiency for vital nodes identification in complex networks

A modified gravity model based on network efficiency for vital nodes identification in complex networks

A Map-Based Recommendation System and House Price Prediction Model for Real Estate

Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

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