Inclusion of Semantic and Time-Variant Information Using Matrix Factorization Approach for Implicit Rating of Last.Fm Dataset

Kushwaha, Nidhi; Mehrotra, Shubham; Kalia, Ronish; Kumar, D. Arul; Vyas, O. P.

doi:10.1007/s13369-016-2209-0

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The Matrix Factorization (MF) algorithm is a state-of-the-art technique for predicting ratings for unseen items, especially when data are sparse [15]. The accuracy of MF is higher compared to baseline techniques [16,17,18]. One of the most powerful features of MF is that it allows for taking advantage of the latent spaces of both users and items.…”

Section: Matrix Factorizationmentioning

confidence: 99%

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Friend Recommendation Engine for Facebook Users via Collaborative Filtering

Alshammari

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Today’s internet consists of an abundant amount of information that makes it difficult for recommendation engines to produce satisfying outputs. This huge stream of unrelated data increases its sparsity, which makes the recommender system’s job more challenging. Facebook’s main recommendation task is to recommend a friendship connection based on the idea that a friend of a friend is also a friend; however, the majority of recommendations using this approach lead to little to no interaction. We propose a model using the matrix factorization technique that leverages interactions between Facebook users and generates a list of friendship connections that are very likely to be interactive. We tested our model using a real dataset with over 33 million interactions between users. The accuracy of the proposed algorithm is measured using the error rate of the predicted number of interactions between possible friends in comparison to the actual values.

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Section: Matrix Factorizationmentioning

confidence: 99%

“…Another model using MF in the field of recommender systems is the one proposed by [18]. In this case, the researchers developed [17] their model by including semantic web technology in the process of building the engine.…”

Section: Matrix Factorizationmentioning

confidence: 99%

Friend Recommendation Engine for Facebook Users via Collaborative Filtering

Alshammari

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Kushwaha et al [6] developed the approach of [5] by exploiting the power of the semantic web. In this study, the authors tested the proposed method in two domains: music and movies.…”

Section: Related Work a Black Box Recommender Systemsmentioning

confidence: 99%

Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems

2019

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Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered stateof-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without compromising trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. The objective of this work is to build a recommender system that can generate both accurate predictions and semantically rich explanations that justify the predictions. We propose a novel approach to build an explanation generation mechanism into a latent factor-based black box recommendation model. The designed model is trained to learn to make predictions that are accompanied by explanations that are automatically mined from the semantic web. Our evaluation experiments, which carefully study the trade-offs between the quality of predictions and explanations, show that our proposed approach succeeds in producing explainable predictions without a significant sacrifice in prediction accuracy.

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“…Next, we will explore two matrix factorization approaches presented by [94,95], who improved the basic matrix factorization technique, which we described above, and came up with new approaches, JMF and SemJMF. Then, we will explain three other types of matrix factorization models used in the field: the SVD, SVD++, and time-aware factor models.…”

Section: Matrix Factorization Modelsmentioning

confidence: 99%

“…They stimulate the idea of water ripple propagation on understanding the user preferences by iteratively considering more side information and propagating the user interests and showed that their model performed better than state-of-the-art models. In section 2, we discussed study number 15 [95] in Table 2 [133], was employed to enrich the resources' similarity matrix by including more indirect links in the calculation of the semantic distance between resources. The system is called…”

Section: Explanation Styles and Related Approachesmentioning

confidence: 99%

An explainable recommender system based on semantically-aware matrix factorization.

Alshammari¹

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Inclusion of Semantic and Time-Variant Information Using Matrix Factorization Approach for Implicit Rating of Last.Fm Dataset

Cited by 5 publications

References 17 publications

Friend Recommendation Engine for Facebook Users via Collaborative Filtering

Friend Recommendation Engine for Facebook Users via Collaborative Filtering

Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems

An explainable recommender system based on semantically-aware matrix factorization.

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