Recommender systems based on user reviews: the state of the art

Chen, Li; Chen, Guanliang; Wang, Feng

doi:10.1007/s11257-015-9155-5

Cited by 312 publications

(202 citation statements)

References 96 publications

(217 reference statements)

Supporting

Mentioning

187

Contrasting

Unclassified

Order By: Relevance

“…Additionally, content-based RSs still suffer from the recommendations' limited diversity and overspecialization problems, which limit the items recommended to users only to similar items that were previously rated. Thus, users cannot find something unexpected [3], [7], [9].…”

Section: Recommender Systemsmentioning

confidence: 99%

“…Symmetric similarity metric is used if there is no bias in favor of either the upper or the lower value of a numeric feature; otherwise, asymmetric will be used. Moreover, evaluating the similarity of nominal features necessitates extra domain knowledge [1], [7]. Examples of local feature-based similarity measures are presented in Table 2 where Max i and Min i refer to the maximum and the minimum values of the i-th feature.…”

Section: S Imilarity(t C)mentioning

confidence: 99%

“…Data acquired from reviews are used to enhance RSs whether the data are sparse or not by providing further information about user preferences [7]. Case-based RSs usually represent cases (products) based on the offered features in product catalogs.…”

Section: Customer-oriented Review Recommendationmentioning

confidence: 99%

“…Moreover, it will increase the quality and accuracy of product ranking. Thus, products with higher sentiment values can be recommended to the user [7].…”

Section: Customer-oriented Review Recommendationmentioning

confidence: 99%

“…The system can then take the multi-sided nature of the customers' opinions from their reviews and therefore construct a preference model for products. This cannot be done with recommendation approaches that use overall ratings [7].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A New Sentiment Case-Based Recommender

Aldayel

Ykhlef

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYRecommender systems have attracted attention in both the academic and the business areas. They aim to give users more intelligent methods for navigating and identifying complex information spaces, especially in e-commerce domain. However, these systems still have to overcome certain limitations that reduce their performance, such as overspecialization of recommendations, cold-start, and difficulties when items with unequal probability distribution exist. A novel approach addresses the above issues through a case-based recommendation methodology which is a form of content-based recommendation that is well suited to many product recommendation domains, owing to the clear organization of users' needs and preferences. Unfortunately, the experience-based roots of case-based reasoning are not clearly reflected in case-based recommenders. In other words, the concept that product cases, which are usually fixed feature-based tuples, are experiential is not adopted well in case-based recommenders. To solve this problem as well as the recommenders' rating sparsity issue, one can use product reviews which are generated from users' experience with the product a basis of product information. Our approach adapts the use of sentiment scores along with feature similarity throughout the recommendation unlike traditional case-based recommender systems, which tend to depend entirely on pure similarity-based approaches. This paper models product cases with the products' features and sentiment scores at the feature level and product level. Thus, combining user experience and similarity measures improves the recommender performance and gives users more flexibility to choose whether they prefer products more similar to their query or better qualified products. We present the results using different evaluation methods for different case structures, different numbers of similar cases retrieved and multilevel sentiment-approaches. The recommender performance was highly improved with the use of feature-level sentiment approach, which recommends product cases that are similar to the query but favored for customers. key words: recommenders, case-based reasoning (CBR), case-based recommenders, opinion mining

show abstract

Section: Recommender Systemsmentioning

confidence: 99%

Section: S Imilarity(t C)mentioning

confidence: 99%

Section: Customer-oriented Review Recommendationmentioning

confidence: 99%

“…Moreover, it will increase the quality and accuracy of product ranking. Thus, products with higher sentiment values can be recommended to the user [7].…”

Section: Customer-oriented Review Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Sentiment Case-Based Recommender

Aldayel

Ykhlef

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

View full text Add to dashboard Cite

Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

show abstract

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Alagappan

Victor

2022

Concurrency and Computation

View full text Add to dashboard Cite

Web-page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph-based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot-based dynamic graph model obtains the user-web page relationship. Weight calculation of user-item and the user-user edge is also processed with this graph model. The user-user edge similarity is used to obtain the user preference vector. Then, unsupervised-deep auto encoder based density-based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. "All the news", MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database. K E Y W O R D S clustering, density based clustering, dynamic graph-based model, ranking, unsupervised-deep auto encoder, user preference vector 1 INTRODUCTION Recently, recommendation systems (RSs) are largely utilized in several web related applications. The RS is one of the software platforms that renders different kinds of ideas to the user depending on their requirement. RS is also called an information filtering mechanism, which seems like what type of music to listen to, what type of book to read, what type of things to buy and so forth. 1 In the internet, the overloading of information is

show abstract

Recommender systems based on user reviews: the state of the art

Cited by 312 publications

References 96 publications

A New Sentiment Case-Based Recommender

A New Sentiment Case-Based Recommender

Towards the significance of taxi recommender systems in smart cities

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Contact Info

Product

Resources

About