Evolution of recommender system over the time

Sinha, Bam Bahadur; Dhanalakshmi, R.

doi:10.1007/s00500-019-04143-8

Cited by 25 publications

(7 citation statements)

References 118 publications

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“…in Equations ( 2) and ( 3) is generally found using different techniques such as cosine, jaccard, 18 Pearson correlation coefficient (PCC) 19 etc. In the Graph k-medoids approach, based on the number of hops between nodes, all the nodes in the graph are assigned to the nearest center and the process continues until the cluster converges.…”

Section: K-medoid Clusteringmentioning

confidence: 99%

Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing

Agarwal¹,

Sinha

2022

Concurrency and Computation

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Web is becoming rich in data. Some of the sources from where these data are originating includes Blogs, YouTube, Twitter, Emails, E-commerce, Banking, sensors, and the Internet of Things. But these data are structured in a very poor fashion. The content of the web is becoming heterogeneous in nature both in terms of compendium and structure. It can be said that these data are human-readable data but the main motive is to draw inferences from these data which is only possible if it can be made machine-accessible. Clustering is considered an important task to organize these data and draw meaningful inferences from these data. In this paper, a clustering approach is proposed that can be applied to knowledge graphs and the possibility of applying Locality Sensitive Hashing is explored. Given the size of linked data, it is observed that this approach can be effective and scalable in comparison to other clustering approaches such as Hierarchical clustering, K-Means clustering, and K-Medoid clustering in discovering different communities that are defined by the link structure of the graph. The experimental results on different types of Linked Data sources justify the efficacy of the proposed model in terms of scalability and efficiency.

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Section: K-medoid Clusteringmentioning

confidence: 99%

Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing

Agarwal¹,

Sinha

2022

Concurrency and Computation

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“…Collaborative filtering (CF) approach recommends the items to the target user on the basis of his past preferences, and filtering out other users with similar behaviors [Villegas et al 2018, Sinha andDhanalakshmi 2019]. For the CF recommendation method, the user profile is built by filtering information from the user's behaviors like ratings, assigned tags and comments or implicitly rating by liking the items.…”

Section: Recommender System Approachesmentioning

confidence: 99%

Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Qassimi¹,

Abdelwahed²

2021

jucs

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Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

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“…Hence, the selected neighbourhood by HAR-KNN is similar to achieving the target products by the traditional methods. Moreover, the accuracy of the recall, F1 measure, and precision are computed to rate the relevant products and number of recommendations that were set to [2,4,6,8,10,12]. Therefore, the HAR-KNN provides better RS with better predictive accuracy than existing methods for the Amazon dataset.…”

Section: Case Studymentioning

confidence: 99%

“…Traditional retail can present only popular products, but online can present a variety of products. Due to the enormous information available across the web, it is difficult for users to comprehend whether the items presented by recommender frameworks are accurate or not [2].…”

Section: Introductionmentioning

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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Evolution of recommender system over the time

Cited by 25 publications

References 118 publications

Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing

Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing

Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

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

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