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.
Taxonomy-aware Recommender Systems. There has been some effort to investigate taxonomy-aware' recommendation models, including earlier works extending neighborhood-based methods (e.g. @cite_5 @cite_22 ) and more recent endeavors to extend MF using either explicit (e.g. @cite_13 ) or implicit (e.g. @cite_12 @cite_23 @cite_24 ) taxonomies. This work is related to ours, though does not consider the visual appearance of products as we do here.
@cite_22 @cite_24 @cite_23 @cite_5 @cite_13 @cite_12
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Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework. Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years. At the same time, very large taxonomies for product classification are becoming increasingly prominent among e-commerce systems for diverse domains, rendering detailed machine-readable content descriptions feasible. Amazon.com makes use of an entire plethora of hand-crafted taxonomies classifying books, movies, apparel, and various other goods. We exploit such taxonomic background knowledge for the computation of personalized recommendations. Hereby, relationships between super-concepts and sub-concepts constitute an important cornerstone of our novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen. Ample empirical analysis, both offline and online, demonstrates our proposal's superiority over common existing approaches when user information is sparse and implicit ratings prevail. We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items that are sampled from the same distribution. Using the item relationship information from the provided taxonomy to constrain item representations results in improved predictive performance. Providing the model with features summarizing the user's rating history as it relates to the item being ranked leads to further gains, producing the best single model result on Track 2. Personalized recommender systems based on latent factor models are widely used to increase sales in e-commerce. Such systems use the past behavior of users to recommend new items that are likely to be of interest to them. However, latent factor model suffer from sparse user-item interaction in online shopping data: for a large portion of items that do not have sufficient purchase records, their latent factors cannot be estimated accurately. In this paper, we propose a novel approach that automatically discovers the taxonomies from online shopping data and jointly learns a taxonomy-based recommendation system. Out model is non-parametric and can learn the taxonomy structure automatically from the data. Since the taxonomy allows purchase data to be shared between items, it effectively improves the accuracy of recommending tail items by sharing strength with the more frequent items. Experiments on a large-scale online shopping dataset confirm that our proposed model improves significantly over state-of-the-art latent factor models. Moreover, our model generates high-quality and human readable taxonomies. Finally, using the algorithm-generated taxonomy, our model even outperforms latent factor models based on the human-induced taxonomy, thus alleviating the need for costly manual taxonomy generation.