Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems. Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space. This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones. Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings. In addition, CGH initiates a new marketing strategy through mining potential users by a generative step. To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing. With the explosion of e-commerce, most customers are accustomed to receiving a variety of recommendations, such as movies, books, news, or hotels they might be interested in. Traditional recommender systems just recommended items that are similar to what they liked or rated in the previous. Recommendations help users find their desirable items, and also creates new revenue opportunities for vendors, such as Amazon, Taobao, eBay, etc. Among them, one of the most popular recommendation methods, collaborative filtering is dependent on a large amount of user-item interactive information to provide an accurate recommendation. However, most of new e-commerce vendors do not have enough interactive data, which leads to low recommendation accuracy, i.e., cold-start issues. Previous studies on cold-start issues generally modeled as a combination of collaborative filtering and content filtering, known as hybrid recommender systems. Specifically, they learned real latent factors by incorporating the side information into the interactive data. Such as Collaborative Deep Learning (CDL) (Wang et al., 2015) , Visual Bayesian Personalized Ranking (VBPR) (He & McAuley, 2016) , Collaborative Topic modeling for Recommedation (CTR) (Wang & Blei, 2011) , and the DropoutNet for addressing cold start (DropoutNet) (Volkovs et al., 2017) , ABCPRec for Bridging Consumer and Producer Roles for User-Generated Content Recommendation (ABCPRec) (Tsukuda et al., 2019) . All of the above hybrid recommender systems were modeled in real latent space, which leads to low efficiency for the online recommendation with the increasing scale of datasets. discrete objectives. Thus many scholars learned binary codes by some approximate techniques, such as the two-stage hashing learning method utilized in Preference Preserving Hashing(PPH) and the Iterative Quantization(ITQ) (Zhou & Zha, 2012) . To reduce information loss, two learning-based hashing frameworks: bit-wise learning and block-wise learning were respectively proposed in hashing based recommendation frameworks (Zhang et al., 2016; Zhang et al., 2018; . However, due to the requirement of binary outputs for learning-based hashing frameworks, the training procedure is expensive for large-scale recommendation, which motivates us to propose a generative approach to learn hash functions. In this paper, we propose the collaborative generated hashing(CGH) to learn hash functions of users and items from content data with the principle of Minimum Description Length (MDL) (Dai et al., 2017) . In marketing area, mining potential customers is crucial to the e-commerce. CGH provides a strategy to discover potential users by the generative step. To reconstruct effective users, uncorrelated and balanced limits are imposed to learn compact and informative binary codes with the principle of the MDL. Especially, discovering potential customers is vital to the success of adding new items for a recommendation platform (Papies et al., 2017) . Specifically, for a new item, we can generate a new potential user by the generative step (detailed in Section 2.1), and then search the nearest potential users in the user set. By recommending a new product to the potential users who might be interested in but didn't plan to buy, further e-commerce strategies can be developed to attract those potential users. We organize the paper as follows: Section 2 introduce the main techniques of CGH. We first introduce the framework of CGH and compare it with the closely related competing baselines: CDL (Wang et al., 2015) and DropoutNet (Volkovs et al., 2017) ; we then formulate the generative step in Section 2.1 and the inference step in Section 2.2, respectively; we finally summarize the training objective and introduce the optimization in Section 2.3. Particularly, we demonstrate the process of mining potential users for the marketing application in Section 2.1. Section 3 presents the experimental results for marketing analysis and recommendation accuracy in various settings. Section 4 concludes the paper. The main contributions of this paper are summarized as follows: (1) We propose the Collaborative Generated Hashing (CGH) with the principle of MDL to learn compact but informative hash codes, which applies to various settings for recommendation. (2) We provides a marketing strategy by discovering potential users by the generative step of CGH, which can be applied to boost the e-commence development. (3) We evaluate the effectiveness of the proposed CGH compared with the state-of-the-art baselines, and demonstrate its robustness and convergence properties on the public datasets. In this paper, a generated recommendation framework called collaborative generated hashing (CGH) is proposed to address the cold-start and efficiency issues for recommendation. The two main contributions are put forward in this paper: (1) we develop a collaborative generated hashing framework with the principle of Minimum Description Length together(MDL) with uncorrelated and balanced constraints on the inference process to derive compact and informative hash codes, which is significant for the accuracy of recommendation and marketing; (2) we propose a marketing strategy by the proposed CGH, specifically, we design a framework to discover the k potential users by the generate step; (3) we evaluate the proposed scheme on two the public datasets, the experimental results show the effectiveness of the proposed CGH for both warm-start and cold-start recommendation.
It can generate effective hash codes for efficient cold-start recommendation and meanwhile provide a feasible marketing strategy.
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