Personalized Item Recommendation for Second-hand Trading Platform

Yu, Xuzheng; Gan, Tian; Wei, Yinwei; Cheng, Zhiyong; Nie, Liqiang

doi:10.1145/3394171.3413640

Cited by 27 publications

(22 citation statements)

References 23 publications

(24 reference statements)

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“…Comparing with the work focusing on explicit feedback [5,18,41], profiling the user from implicit feedback is more practical and challenge. Therefore, researchers shift to explore the user-item interaction from implicit feedback data.…”

Section: Recommendation With Implicit Feedbackmentioning

confidence: 99%

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Wei¹,

Wang

Nie³

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

Self Cite

207

201

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Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element of GCNs to perform information propagation and generate informative representations. Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with lessinterested items occur in implicit feedback (say, a user views micro-videos accidentally). This means that the neighborhoods involved with such false-positive edges will be influenced negatively and the signal on user preference can be severely contaminated. However, existing GCN-based recommender models leave such challenge under-explored, resulting in suboptimal representations and performance.In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommender model, Graph-Refined Convolutional Network (GRCN), which adjusts the structure of interaction graph adaptively based on status of model training, instead of remaining the fixed structure. In particular, a graph refining layer is designed to identify the noisy edges with the high confidence of being false-positive interactions, and consequently prune them in a soft manner. We then apply a graph convolutional layer on the refined graph to distill informative signals on user preference. Through extensive experiments on three datasets for micro-video recommendation, we validate the rationality and effectiveness of our GRCN. Further in-depth analysis presents how the refined graph benefits the GCNbased recommender model.

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Section: Recommendation With Implicit Feedbackmentioning

confidence: 99%

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Wei¹,

Wang

Nie³

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

Self Cite

207

201

View full text Add to dashboard Cite

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“…Xavier [9] and Adam [18] algorithms are utilized in the experiments to initialize and optimize the parameters. In terms of the hyperparameters, we use the grid search [16,44]: the learning rate is tuned in {0.0001, 0.001, 0.01, 0.1} and regularization weight is searched in {0.0001, 0.001, 0.01, 0.1}. Besides, we set the embedding dimension as 64 and stop the training if recall@10 on the validation data does not increase for 10 successive epochs.…”

Section: Parameter Settingsmentioning

confidence: 99%

Contrastive Learning for Cold-Start Recommendation

Wei

Xiang

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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184

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Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g., content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance on cold-start recommendation.In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet efficient Contrastive Learning-based Cold-start Recommendation framework (CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules.

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“…For fairness, we set the dimension of the collaborative embedding as 64 for all models. In terms of the hyper-parameters, we use the grid search [20,51]: the learning rate is tuned in {0.0001, 0.001, 0.01, 0.1} and regularization weight is searched in {0.0001, 0.001, 0.01, 0.1}. Besides, we employ the early stopping strategy [18], which stops the training if recall@10 on the validation data does not increase for Negative# denotes the number of negative pairs.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Contrastive Learning for Cold-Start Recommendation

Wei

Wang

Li³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g., content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance on cold-start recommendation.In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet effective Contrastive Learning-based Cold-start Recommendation framework (CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules. It allows us to preserve collaborative signals in the content representations for both warm and cold-start items. Through extensive experiments on four publicly accessible datasets, we

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Personalized Item Recommendation for Second-hand Trading Platform

Cited by 27 publications

References 23 publications

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Contrastive Learning for Cold-Start Recommendation

Contrastive Learning for Cold-Start Recommendation

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