Semantically Enhanced Collaborative Filtering Based on RSVD

Szwabe, Andrzej; Ciesielczyk, Michal; Janasiewicz, Tadeusz

doi:10.1007/978-3-642-23938-0_2

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Semantic-based service recommendation is to mine users's semantic information to improve the users' recommendation experience. Khan et al 24 extracted the interaction between users and preferences through latent semantic models, and provided users with searching preferences according to their interests. Cantador et al 25 proposed an ontology-based semantic model to describe the semantics of users' interests and items, and added context to this semantic model, which can alleviate the cold start and data sparsity problems in recommendation and help users find items matching their interests more quickly.…”

Section: Semantic-based Service Recommendationmentioning

confidence: 99%

“…It only needs the simple interaction information (e.g., scoring and praising) between users and services to provide decisions for user service invocation. Researchers have shown keen interest in score predictions, [7][8][9][10] and proposed many collaborative filtering algorithms from different angles, such as neighborhood-based collaborative filtering 7,11 and model-based collaborative filtering. 12 The key of neighborhood-based collaborative filtering is to select similar neighbors for each target user and target item, predict the scores, and recommend users or items on the basis of the target objects.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid intelligent service recommendation by latent semantics and explicit ratings

Duan

Gao

et al. 2021

Int J of Intelligent Sys

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User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting userservice ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category Beijing Jiaotong University, Grant/Award Number: 2019RC050 to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set.Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods.

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Section: Semantic-based Service Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid intelligent service recommendation by latent semantics and explicit ratings

Duan

Gao

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

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“…A combination of singular value decomposition (SVD) and random indexing as a hybrid method was also proposed in the literature. This method incorporates the management of data by monitoring the item description and users' behavior [23]. Moreover, probabilistic matrix factorization [24], principal component analysis (PCA) [25], singular value decomposition [26], cluster-based smoothing method [27], Neural Networking, are among the significant approaches used to enhance the effectiveness of CF through solving various computational setbacks.…”

Section: Wherementioning

confidence: 99%

“…The readings from the recall score must be high to achieve the best efficiency. The following formula is used to calculate recall: Precision= (23) Recall= (24) In Eq (23), n is the number of items appearing in the recommended list and relevant to the testing user. In Eq (24), is the total number of relevant items in the testing set.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Improved Product Recommendation Method for Collaborative Filtering

et al. 2020

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Collaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix. Based on the computed similarity, a prediction is made for the unknown or new product. There are many similarity computation methods, such as the Pearson correlation coefficient (PCC), Jaccard, Mean square difference, Cosine, etc. however, the accuracy of product recommendations using these methods is not very promising. This work introduces an improved product recommendation method for collaborative filtering, which is based on the triangle similarity. However, the downside of triangle similarity is that it only considers the common ratings of users. The proposed similarity measure not only focuses on common ratings but also consider the ratings of those items that are not commonly rated from pairs of users. The obtained similarity is further complemented with the user rating preference (URP) behavior in giving rating preferences. To evaluate the accuracy, experiments are performed on the six commonly used datasets in the field of CF. Experimental results prove that the proposed similarity measure performs well as compared to the existing similarity measures. INDEX TERMS Collaborative filtering, recommender systems, triangle similarity, user preferences.

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“…To improve the accuracy of CF algorithms, some novel methods have been proposed. Szwabe et al [24] proposed a hybrid recommendation approach based on two-stage data processing, using a random indexing technique and singular-value decomposition (SVD) to preprocess content features such as item traits and user behaviour data. In Hao et al [25], probabilistic matrix factorisation was utilised to address the sparsity problem in social recommendation systems.…”

Section: Basic Concepts and Related Workmentioning

confidence: 99%

A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data

Deng

Wang

Guo

et al. 2018

Journal of Information Science

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In the neighbourhood-based collaborative filtering (CF) algorithms, a user similarity measure is used to find other users similar to an active user. Most of the existing user similarity measures rely on the co-rated items. However, there are not enough co-rated items in sparse dataset, which usually leads to poor prediction. In this article, a new similarity scheme is proposed, which breaks free of the constraint of the co-rated items. Moreover, an item similarity measure based on the Kullback–Leibler (KL) divergence is presented, which identifies the relation between items based on the probability density distribution of ratings. Since the item similarity based on KL divergence makes full use of all ratings, it owns better flexibility for sparse datasets. The CF algorithm using our proposed similarity scheme is implemented and compared with some classic CF algorithms. The compared results show that the CF using our similarity has better predictive performance. Therefore, our similarity scheme is a good solution for the sparsity problem and has great potential to be applied to recommendation systems.

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Semantically Enhanced Collaborative Filtering Based on RSVD

Cited by 13 publications

References 15 publications

A hybrid intelligent service recommendation by latent semantics and explicit ratings

A hybrid intelligent service recommendation by latent semantics and explicit ratings

An Improved Product Recommendation Method for Collaborative Filtering

A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data

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