Neural Personalized Ranking for Image Recommendation

Niu, Wei; Caverlee, James; Lu, Haokai

doi:10.1145/3159652.3159728

Cited by 108 publications

(46 citation statements)

References 28 publications

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“…Learning to rank methods have been introduced to optimize recommendation systems toward personalized ranking. Inspired by recent success of Bayesian Personalized Ranking (BPR) [42] in image and friend recommendation systems [13,35], we choose BPR aver other approaches. The idea behind BPR is that observed useritem interactions should be ranked higher than unobserved ones.…”

Section: Bayesian Personalized Recommendationmentioning

confidence: 99%

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Beigi

Mosallanezhad

Guo

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users' private-attribute information according to their items list and recommendations. The recommender aims to extract users' interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks. CCS CONCEPTS• Information systems → Recommender systems; • Security and privacy → Social network security and privacy; Privacy protections.

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Section: Bayesian Personalized Recommendationmentioning

confidence: 99%

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Beigi

Mosallanezhad

Guo

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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“…E.g., VBPR is an extension of BPR for image recommendation, on top of which it learned an additional visual dimension from CNN that modeled users' visual preferences [18]. There are some other image recommendation models that tackled the temporal dynamics of users' preferences to images over time [17], or users' location preferences for image recommendation [35], [49], [35]. As well studied in the computer vision community, in parallel to the visual content information from deep CNNs, images convey rich style information.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Attention Model for Social Contextual Image Recommendation

Chen

Hong

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information (e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users' preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model. !

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“…Neural-Based Recommendation with Implicit Feedback. To make recommendations in such sparse, implicit feedback scenarios, methods like Bayesian Personalized Recommendation (BPR) [21] and a recently introduced variant called Neural Personalized Ranking (NPR) [18] have shown good success. These and other neural approaches have demonstrated their power in recommenders, including [4,6,25].…”

Section: Related Workmentioning

confidence: 99%

Recommending Music Curators: A Neural Style-Aware Approach

Wang

Caverlee

2020

Lecture Notes in Computer Science

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We propose a framework for personalized music curator recommendation to connect users with curators who have matching curation style. Three unique features of the proposed framework are: (i) models of curation style to capture the coverage of music and curator's individual style in assigning tracks to playlists; (ii) a curation-based embedding approach to capture inter-track agreement, beyond the audio features, resulting in models of music tracks that pair well together; and (iii) a novel neural pairwise ranking model for personalized music curator recommendation that naturally incorporates both curator style models and track embeddings. Experiments over a Spotify dataset show significant improvements in precision, recall, and F1 versus state-of-the-art.

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Neural Personalized Ranking for Image Recommendation

Cited by 108 publications

References 28 publications

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

A Hierarchical Attention Model for Social Contextual Image Recommendation

Recommending Music Curators: A Neural Style-Aware Approach

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