A mobile tourist assistance and recommendation system based on complex networks

Schering, Alf-Christian; Dueffer, Martin; Finger, Andreas; Bruder, Ilvio

doi:10.1145/1651274.1651290

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…In [40], the bipartite graph model is used to describe the relationship between customers and products, which improves the recommendation quality of the spreading activation algorithm to a certain extent. A Complex network is used to implement digital tourism assistance, which can recommend optimal routes and provide mobile guidance to the users in [41]. [42] use the standard complex network technology in the sample of social musician network and developed a derived complex network by confirming attribute similarity.…”

Section: Complex Network Recommender Systemsmentioning

confidence: 99%

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

Bai¹,

Jia²,

Wang³

et al. 2020

Preprint

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A good recommender system can infer customers' preferences based on their historical purchase records, and recommend products that the customers may be interested in, saving them a lot of time and energy. For enterprises, it is difficult to recommend accurately to each customer, and the bad recommendation may be counterproductive. Customer loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. A hypothesis is proposed in this study: if companies can divide customers into different groups based on customer loyalty, the recommendation effect on certain groups is better than that on overall customers.In this study, customer loyalty is measured by four features of the RFML model. All customers are viewed as points on a four-dimensional space, which are clustered by the k-means model. Two recommendation algorithms based on complex networks are tested: recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and recommendation algorithm based on a single vertex set network and DeepWalk (SVDW). The experimental results show that customer loyalty has improved the effectiveness of the two algorithms over 14%, and the recommendation effect is the best on customer groups with a loyalty level of 4 (the highest level is 5). The recommendation algorithms with customer loyalty are better than using them alone.

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Section: Complex Network Recommender Systemsmentioning

confidence: 99%

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

Bai¹,

Jia²,

Wang³

et al. 2020

Preprint

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“…The Context-Aware Recommender System feeds on information such as the location of the user, Identity of people the around, the date, the season, the temperature, and other related data to make recommendations. This technique is seen to be more precise and accurate [5]. For example, watching a movie: time, location, companion, emotions, occasion.…”

Section: Context-awarementioning

confidence: 99%

“…A Recommendation System is an intelligent system that is capable of suggesting items of interest to a person based on an underlining algorithm that learns from data [2]- [5]. Recommender systems are utilized in a variety of areas including movies, games, music, news, books, dating sites, research articles, social tags, and other kinds of products.…”

Section: Introductionmentioning

confidence: 99%

Complex Network based Recommender System

Kingsley¹,

Francis²,

Appiah³

et al. 2019

IJCA

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The structural abstraction of Recommender Systems has a high resemblance as that of a Complex Network(CN). The underlining principles of the different types of Recommender Systems, such as Collaborative Filtering, Content-Based, Knowledge-Based, Utility-Based, and Hybrid are similar to the attributes and theories of Complex Networks. Considering the enormity of computation in Recommender Systems logic formulation and it's a structural similarity with Complex Networks, the research work seeks to highlight attributes of Complex Networks and Recommender Systems to suggest the applicability of theories, and principles in CN's that can be considered in the construction of Recommender Systems. The research further elaborates on matrices for checking the correctness of the use of these theories.

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“…In these experiences there are various points of interest (POI) for tourists that are connected to, or anchored to, digital content. Such systems may provide other forms of context-aware interaction [19] such as personalization of information based on previous places visited by the tourists, or other attributes such as what content people are interested in [24], [25], [26].…”

Section: Introductionmentioning

confidence: 99%