ImRec: Learning Reciprocal Preferences Using Images

Neve, James; McConville, Ryan

doi:10.1145/3383313.3411476

Cited by 9 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RRS is frequently employed in domains with significant societal influences, such as recruitment [25,38,46], online dating [30,39,42] and social networking platforms [10]. They can be divided into two main categories based on the data source they used: content-based methods based on user profiles [26,30] and collaborative filtering methods based on user behaviors [16,27,42]. As mentioned above, person-job fit methods based on text matching [44,55] can also be regarded as contentbased reciprocal recommender systems.…”

Section: Recommender Systemsmentioning

confidence: 99%

Modeling Two-Way Selection Preference for Person-Job Fit

Chen¹,

Hou²,

Song³

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dualperspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN.

show abstract

Section: Recommender Systemsmentioning

confidence: 99%

Modeling Two-Way Selection Preference for Person-Job Fit

Chen¹,

Hou²,

Song³

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

View full text Add to dashboard Cite

show abstract

“…With more deep learning-based recommendation models being developed [38][39][40][41], recent methods can provide a more complicated modeling of the user-item interaction with the visual feature in addition to the simple retrieval-based approaches [7,9,14,19,28,29,31,48,57]. These methods mainly rely on pretrained deep learning framework to incorporate the visual knowledge, e.g., ResNet [12] and VGG [47].…”

Section: Visually-aware Recommendationmentioning

confidence: 99%

“…Besides passively using the existing visual features, DVBPR [19] applies an end-to-end trained CNN instead of the pretrained backbone for visual feature extraction. ImRec [31] proposes to use the reciprocal information between user groups with the aid of the image features.…”

Section: Visually-aware Recommendationmentioning

confidence: 99%

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

Qiu

Wang

Chen

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

View full text Add to dashboard Cite

Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference for these items in addition to the historical user-item interaction records. It is commonly observed that user's attention to visual features does not always reflect the real preference. Although a user may click and view an item in light of a visual satisfaction of their expectations, a real purchase does not always occur due to the unsatisfaction of other essential features (e.g., brand, material, price). We refer to the reason for such a visually related interaction deviating from the real preference as a visual bias. Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation. In this paper, we derive a causal graph to identify and analyze the visual bias of these existing methods. In this causal graph, the visual feature of an item acts as a mediator, which could introduce a spurious relationship between the user and the item. To eliminate this spurious relationship that misleads the prediction of the user's real preference, an intervention and a counterfactual inference are developed over the mediator. Particularly, the Total Indirect Effect is applied for a debiased prediction during the testing phase of the model. This causal inference framework is model agnostic such that it can be integrated into the existing methods. Furthermore, we propose a debiased visuallyaware recommender system, denoted as CausalRec to effectively retain the supportive significance of the visual information and remove the visual bias. Extensive experiments are conducted on eight benchmark datasets, which shows the state-of-the-art performance of CausalRec and the efficacy of debiasing. CCS CONCEPTS• Information systems → Recommender systems.

show abstract

“…Algorithms also incorporate the visual signal into CF models so as to exploit user feedback and visual features simultaneously, e.g., VBPR [20] and Fashion DNA [4]. Recently, with the advances in computational resources, learningbased neural frameworks have been proposed and achieve state of the art performance on fashion recommendation (DVBPR [24]) and reciprocal recommendations (ImRec [34]). In our paper, to comprehensively evaluate AIP attacks in different recommenders, we select three representatives: an image-retrieval-based similarity model, a CF model leveraging visual features and a learning-based neural model.…”

Section: Visually-aware Recommender Systemmentioning

confidence: 99%

Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start

Liu

Larson

2021

Proceedings of the Web Conference 2021

View full text Add to dashboard Cite

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to appear as high as possible in the top-N of these ranked lists. In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. Recommender systems that use images to address the cold start problem are vulnerable to this security risk. We describe a new type of attack, Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N recommenders: the ranking mechanism itself. Existing work on adversarial images in recommender systems investigates the implications of conventional attacks, which target deep learning classifiers. In contrast, our AIP attacks are embedding attacks that seek to push features representations in a way that fools the ranker (not a classifier) and directly leads to item promotion. We introduce three AIP attacks insider attack, expert attack, and semantic attack, which are defined with respect to three successively more realistic attack models. Our experiments evaluate the danger of these attacks when mounted against three representative visually-aware recommender algorithms in a framework that uses images to address cold start. We also evaluate potential defenses, including adversarial training and find that common, currentlyexisting, techniques do not eliminate the danger of AIP attacks. In sum, we show that using images to address cold start opens recommender systems to potential threats with clear practical implications. CCS CONCEPTS• Information systems → Recommender systems; • Security and privacy → Web application security.

show abstract

ImRec: Learning Reciprocal Preferences Using Images

Cited by 9 publications

References 24 publications

Modeling Two-Way Selection Preference for Person-Job Fit

Modeling Two-Way Selection Preference for Person-Job Fit

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start

Contact Info

Product

Resources

About