SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

Shi, Chuan; Zhang, Zhiqiang; Ji, Yugang; Wang, Weipeng; Yu, Philip S.; Shi, Zhiping

doi:10.1007/s11280-018-0553-6

Cited by 55 publications

(32 citation statements)

References 33 publications

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“…e introduction of the probability model has greatly improved the performance of matrix factorization and further improved the accuracy of the matrix factorization model [28]. Probability matrix decomposition has two leading assumptions: one is that the difference between the overall rating matrix R of the user and the inner product R of the eigenvectors of the user and the movie obeys the Gaussian distribution of variance [29]; the second is that the eigenvector matrix U of the user and the movie's elements of the eigenvector matrix V, respectively, obey the Gaussian distribution with the mean value being 0 and the variance being ϕ u and ϕ v [30,31].…”

Section: Matrix Decomposition Recommendation Algorithmmentioning

confidence: 99%

Personalized Movie Recommendation Method Based on Deep Learning

Liu

Choi

2021

Mathematical Problems in Engineering

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With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.

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Section: Matrix Decomposition Recommendation Algorithmmentioning

confidence: 99%

Personalized Movie Recommendation Method Based on Deep Learning

Liu

Choi

2021

Mathematical Problems in Engineering

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“…Performance comparison experiments using two real-world datasets are presented. These demonstrate that FeatureMF performs better than both the traditional MF models (where no item side-information is taken into account) -as of course might be expected-and more recent the state-of-the-art item information enriched MF-based models [18]- [20].…”

Section: Introductionmentioning

confidence: 56%

FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

et al. 2021

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Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large useritem rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and items has been shown by many both to improve general recommendation performance and to help alleviate the sparsity and cold-start issues in CF. In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF. In this paper, focussing on item side-information, we propose a model that directly incorporates item features with the MF framework in a single step process. The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in the MF model. Results are presented of comparative performance experiments of the model against three state-ofthe-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each. It is shown to yield the best recommendation performance over all these models across all contexts including data sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec. The model is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size.

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“…In their approach, they relied on HIN [24] to generate semantic and justifiable recommendations. Another study that relied on the HIN technique to build a recommender system is SemRec [25]. In this work, the meta-paths obtained from the HIN are personalized and prioritized to accommodate users' preferences.…”

Section: B Explanations In Black Box Recommender Systemsmentioning

confidence: 99%

“…No participant responded with the strongly unsatisfied answer option. Figures 24,25,26,27,28,29,and 30, show the responses of all participants to the demographic questions in Table 12. The answers for these questions were optional.…”

Section: Figure 21mentioning

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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SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

Cited by 55 publications

References 33 publications

Personalized Movie Recommendation Method Based on Deep Learning

Personalized Movie Recommendation Method Based on Deep Learning

FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

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

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