Comparative Deep Learning of Hybrid Representations for Image Recommendations

By Chenyi Lei, Dong Liu, Weiping Li, Zhengjun Zha, Houqiang Li. 2016

117 citations recorded by the source corpus.

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other state-of-the-art image recommendation solutions.

Subfields: Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Yi Tay
5. Le Wu
6. Julian McAuley
7. Kun Zhang
8. Wang-Cheng Kang

Citations in the corpus

• AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020, neutral): “Lei et al.[15] utilized CNN in image recommendation.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “exploit visual signals for constructing item visual representations, and the user preference is represented in the visual space [108], [122], [123], [124], [131], [144], [145], [146], [147].”
• A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “…Wang and Wang (2014) Li et al. (2015) Georgiev and Nakov (2013) Elkahky et al. (2015) Jia et al. (2015) Sedhain et al. (2015) Shin et al. (2015) Lei et al. (2016) Strub and Mary (2015) Zhou et al. (2016) Covington et al. (2016) Du et al. (2016) Shen et al. (2016) Strub et al. (2016) Zheng et…”
• Visually-Aware Fashion Recommendation and Design with Generative Image Models (2017, methodology): “This builds upon recent work on comparative image models [7], [29], though differs in terms of the formulation and training objective used.”
• Artificial intelligence in recommender systems (2020, methodology): “A duel-net deep network was proposed in [193] that directly applies computer vision to image recommendation to map images and user preferences.”
• Deep Learning Based Recommender System (2017, background): “Text Hash Tags [44, 110, 118, 158, 182, 183, 193, 209] News [10, 12, 113, 135, 169, 200] Review texts [11, 87, 126, 146, 174, 197, 202] ‹otes [82, 141] Images Visual features [2, 14, 25, 49, 50, 84, 98, 105, 112, 165, 172, 179, 191, 192, 197, 206] Audio Music [95, 153, 167, 168] Video Videos [14, 17”