Recommendation Based on Review Texts and Social Communities: A Hybrid Model

Ji, Zhenyan; Pi, Huaiyu; Wei, Wei; Xiong, Bo; Woźniak, Marcin; Damaševičius, Robertas

doi:10.1109/access.2019.2897586

Cited by 67 publications

(45 citation statements)

References 22 publications

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“…Ji et al [19] proposed a Hybrid Recommendation model dependent on customer ratings, reviews and social data. This model comprises feature generation, review transformation, model training, community detection, feature blending, and prediction and evaluation.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

et al. 2020

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Recommendation System (RS) has been broadly utilized in various areas and discovers product recommendations during an active user interaction in E-Commerce sites. Tremendous growth of users and products in recent years has faced some key challenges. There are numerous online sites that present many decisions to the user at once, which is strenuous. Moreover, finding active user or right product is an important task in RS. Existing works have been proposed to recommend a product by considering user inclination and socio-demographic behaviour. In this paper, we propose a Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features. It will classify the features using race classifiers from both quality and quantity aspects. The proposed approach also addresses the problems of the previous methods to efficiently evaluate user preference on products and balance feature analysis. The K-NN classification method has been qualified online and real-time to find user behaviour data coordinating to a specific user group containing the relationship between the similarity of many users and target users from a huge amount of data. The proposed experimental result is evaluated based on measures such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Squared Error (RMSE) with the lowest error of 0.7165, 0.7201 and 0.7322 separately. High predictive measures like Precision (P), Recall (R) and F1 are found to have values 0.8501, 0.2201 and 0.3507 respectively. INDEX TERMS Recommendation system (RS), user behaviour data, hybrid filtering, K-NN, behavioural matrix.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

et al. 2020

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“…Every year, the Yelp Inc. Company releases part of their data as an open dataset to grant the scientific community to conduct research and analysis on them. Some interesting articles that use the Yelp dataset for their analysis can be found in [38,43,47,50]. As a use case, we analyzed the 2019 Yelp Challenge dataset [15], containing information about businesses, reviews, and users.…”

Section: Exploring and Analyzing User Reviews: Yelpcommentioning

confidence: 99%

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Spagnuolo¹,

Cordasco²,

Szufel³

et al. 2020

Internet Mathematics

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Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

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“…Nowadays, textual analysis is employed in a wide variety of studies, for example in medicine Lee et al (2019), in tourism management analysis Cheng and Jin (2019), in designing recommendation systems Ji et al (2019), in analyzing countries' foreign policies Cannon et al (2018), in investigating the blog users' sentiments during rainstorm and waterlogging disasters Wu et al (2018) or in understanding the potential applications and users of augmented reality tools Li et al (2018). Feuerriegel and Gordon (2018) highlight the importance of the information contained in written documents to analyse the economic paths and to forecast economic and financial variables.…”

Section: Literature Reviewmentioning

confidence: 99%

The interconnectedness of the economic content in the speeches of the US Presidents

Cinelli¹,

Ficcadenti²,

Riccioni³

2019

Ann Oper Res

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The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents' talks belonging to the network's core share the usage of generic (non-technical) economic locutions like "interest" or "trade". While the use of more technical and less frequent terms characterizes the periphery (e.g. "yield"). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents' speeches.

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Recommendation Based on Review Texts and Social Communities: A Hybrid Model

Cited by 67 publications

References 22 publications

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

The interconnectedness of the economic content in the speeches of the US Presidents

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