Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform extremely well for recommending digital art. Other recent works have shown that explicit visual features (EVF) based on attractiveness can perform well in preference prediction tasks, but no previous work has compared DNN features versus specific attractiveness-based visual features (e.g. brightness, texture) in terms of recommendation performance. In this work, we study and compare the performance of DNN and EVF features for the purpose of physical artwork recommendation using transactional data from UGallery, an online store of physical paintings. In addition, we perform an exploratory analysis to understand if DNN embedded features have some relation with certain EVF. Our results show that DNN features outperform EVF, that certain EVF features are more suited for physical artwork recommendation and, finally, we show evidence that certain neurons in the DNN might be partially encoding visual features such as brightness, providing an opportunity for explaining recommendations based on visual neural models.
Here we survey some works using visual features obtained from pre-trained deep neural networks for recommendation tasks. @cite_2 introduced an image-based recommendation system based on styles and substitutes for clothing using visual embeddings pre-trained on a large-scale dataset obtained from Amazon.com. Recently, @cite_17 went further in this line of research and introduced a visually-aware matrix factorization approach that incorporates visual signals (from a pre-trained DNN) into predictors of people's opinions. The latest work by @cite_12 deals with visually-aware artistic recommendation, building a model which combines ratings, social signals and visual features. @cite_5 compared visual DNN and explicit (stylistic) visual features for movie recommendation, and they found the explicit visual features more informative than DNN features for their recommendation task.
@cite_5 @cite_17 @cite_12 @cite_2
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Modern recommender systems model people and items by discovering or 'teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions. Modern recommender systems model people and items by discovering or teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions. Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup. To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users' decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women's Clothing & Accessories from Amazon, which we use for this demonstration. Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's 'visual preferences,' as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by global' structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously. Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset. Visual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model items in a common visual feature space. In these methods, visual representations always can only capture the categorical information but fail in capturing the styles of items. Style information indicates the preferences of users and has significant effect in visual recommendation. Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users. Experiments conducted on two real-world datasets illustrate the effectiveness of DeepStyle for visual recommendation. Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations. In this paper, we show that user's preferences on movies can be better described in terms of the mise-en-sc ene features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as example of mise-en-sc ene features that can visually describe movies. Interestingly, mise-en-sc ene features can be computed automatically from video files or even from trailers, offering more flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability. We have conducted a set of experiments on a large catalogue of 4K movies. Results show that recommendations based on mise-en-sc ene features consistently provide the best performance with respect to richer sets of more traditional features, such as genre and tag. In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the work of [11] on using variational autoencoders (VAEs) for collaborative filtering with implicit feedback by proposing a hybrid, multi-modal approach. Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation. We empirically show how the VAE network is empowered by incorporating movie embeddings. We also visualize movie and user embeddings by clustering their latent representations obtained from a VAE. With the advent of online social networks, the use of information hidden in social networks for recommendation has been extensively studied. Unlike previous work regarded social influence as regularization terms, we take advantage of network embedding techniques and propose an embedding based recommendation method. Specifically, we first pre-train a network embedding model on the users' social network to map each user into a low dimensional space, and then incorporate them into a matrix factorization model, which combines both latent and pre-learned features for recommendation. The experimental results on two real-world datasets indicate that our proposed model is more effective and can reach better performance than other related methods. In this paper, we present a unified end-to-end approach to build a large scale Visual Search and Recommendation system for e-commerce. Previous works have targeted these problems in isolation. We believe a more effective and elegant solution could be obtained by tackling them together. We propose a unified Deep Convolutional Neural Network architecture, called VisNet, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop dataset. We then share the design decisions and trade-offs made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India's largest e-commerce company. The deployment of our solution has yielded a significant business impact, as measured by the conversion-rate. Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7 relative improvement in WER is reported at -3 SNR dB Emotion recognition is a challenging task because of the emotional gap between subjective emotion and the low-level audio-visual features. Inspired by the recent success of deep learning in bridging the semantic gap, this paper proposes to bridge the emotional gap based on a multimodal Deep Convolution Neural Network (DCNN), which fuses the audio and visual cues in a deep model. This multimodal DCNN is trained with two stages. First, two DCNN models pre-trained on large-scale image data are fine-tuned to perform audio and visual emotion recognition tasks respectively on the corresponding labeled speech and face data. Second, the outputs of these two DCNNs are integrated in a fusion network constructed by a number of fully-connected layers. The fusion network is trained to obtain a joint audio-visual feature representation for emotion recognition. Experimental results on the RML audio-visual database demonstrates the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues in DCNN for emotion recognition. Its success guarantees further research in this direction. Social tagging becomes increasingly important to organize and search large-scale community-contributed photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically assigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tag recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations, we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to individually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by the visual and textual domains to make visual features and textual information of photos comparable. Considering the visual feature is a lower level representation on semantics than the textual information, we adopt a progressive learning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to have consistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediate subspace and the textual space respectively. We formulate the above learning problems into a united form, and present an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user, the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding unified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover the semantically and visually related photos, among which the most frequent tags are used as the recommended tags. Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution. Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and 'appreciates') of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer 'visually consistent' content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves. Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods. Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the 'backbone' of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor-movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features. The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching. The dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it possible to learn useful features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigate if the awareness of egomotion can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We show that given the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on visual tasks of scene recognition, object recognition, visual odometry and keypoint matching. While efforts have been made on bridging the semantic gap in image understanding, the in situ understanding of social media images is arguably more important but has had less progress. In this work, we enrich the representation of images in image tweets by considering their social context. We argue that in the microblog context, traditional image features, e.g., low-level SIFT or high-level detected objects, are far from adequate in interpreting the necessary semantics latent in image tweets. To bridge this gap, we move from the images' pixels to their context and propose a context-aware image bf tweet modelling (CITING) framework to mine and fuse contextual text to model such social media images' semantics. We start with tweet's intrinsic contexts, namely, 1) text within the image itself and 2) its accompanying text; and then we turn to the extrinsic contexts: 3) the external web page linked to by the tweet's embedded URL, and 4) the Web as a whole. These contexts can be leveraged to benefit many fundamental applications. To demonstrate the effectiveness our framework, we focus on the task of personalized image tweet recommendation, developing a feature-aware matrix factorization framework that encodes the contexts as a part of user interest modelling. Extensive experiments on a large Twitter dataset show that our proposed method significantly improves performance. Finally, to spur future studies, we have released both the code of our recommendation model and our image tweet dataset.