Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Hu, Peng; Du, Rong; Hu, Yao; Li, Nan

doi:10.24963/ijcai.2019/350

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…That makes autoencoders well suited for CF-based recommendation problems since there is a need to deal with high sparse data, which includes cold-start users (items). It is noteworthy that, from articles using some autoencoder-based network, almost half of them explored the denoising autoencoder architecture (DAE) (Hu et al, 2019;Jhamb et al, 2018;Liu et al, 2017;Wang et al, 2020;Qingxian Wang et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, exploring a sequential architecture comes as a natural and reasonable choice to learn data dynamics, especially when data representations tend to be sparse. Bellini et al (2017), Chae et al (2019), He et al (2019), Hu et al (2019), Jhamb et al (2018), Lee et al (2017Lee et al ( , 2018, Liang et al (2018), Liu et al (2017), Nisha and Mohan (2019), Song et al (2019), Wang, Chen, et al (2019), Wang et al (2020) Convolutional neural network (CNN) 9 Chen, Cai, et al (2019), Da Costa and Dolog (2019), Hyun et al (2018), Liu et al (2017Liu et al ( , 2019, Wang, Chen, et al (2019), Zhang, Cheng, and Ren (2019), Zhang, Yao, et al (2017), Zheng et al (2017) Generative adversarial network (GAN) 3 Chae et al 2019, Lee et al (2017), Wang, Chen, et al (2019) Graph neural network (GNN) 2 Wu, Hong, et al (2019), Zheng et al (2018) Multilayer perceptron (MLP) 20 Bai et al (2017), Cao et al, 2018, C. Chen et al (2020, L. Chen, Zheng, et al (2018), W. Chen, Cai, et al (2019) , Zhou et al (2019) Neural attention 13 (Cao et al (2018), L. Chen, Zheng, et al, 2018, Chin et al, 2018, W. Fan et al (2019, Feng & Zeng, 2019, Jhamb et al (2018…”

Section: Discussionmentioning

confidence: 99%

“…To do so, a proposed multi-layer structure using LSTM integrates users' random walks paths, latent factors from users, items and ratings intending to better capture from input data the neighbourhood's structure, interaction, and sequential information. Hu et al (2019) propose the Semi-Parametric Embedding (SPE), a hybrid item-item CF approach which incorporates CB information to overcome the cold start problem and models the latent factor simultaneously using an SDAE. An item embedding is composed of two parts, that is, the parametric part from content information and the non-parametric part designed to encode behaviour information (Hu et al, 2019).…”

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

“…Hu et al (2019) propose the Semi-Parametric Embedding (SPE), a hybrid item-item CF approach which incorporates CB information to overcome the cold start problem and models the latent factor simultaneously using an SDAE. An item embedding is composed of two parts, that is, the parametric part from content information and the non-parametric part designed to encode behaviour information (Hu et al, 2019). In this sense, through a simultaneous learning process, the approach can alleviate the lack of data concerning cold start items, thus improving recommendations.…”

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

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Deep learning techniques for recommender systems based on collaborative filtering

2020

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In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-theart literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

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Deep learning techniques for recommender systems based on collaborative filtering

2020

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“…Although the former is a classic solution commonly used by industry [3], it still has many flaws, such as inefficiency, insufficient demand for co-purchaser, and the limited quantity of participants. Compared with the first method, the latter one is a more promising method, as the superiority of the recommendation system has been proven in many other areas [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Co-purchaser Recommendation for Online Group Buying

Chen

Huang

et al. 2020

Data Sci. Eng.

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Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.

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