Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection

Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper coupons which provide customers with discounts or gifts. One of the fundamental problems related is how to deliver e-coupons with minimal cost while users' willingness to place an order is maximized. We call this problem the coupon allocation problem. This is a non-trivial problem since the number of regular users on a mature e-platform often reaches hundreds of millions and the types of e-coupons to be allocated are often multiple. The policy space is extremely large and the online allocation has to satisfy a budget constraint. Besides, one can never observe the responses of one user under different policies which increases the uncertainty of the policy making process. Previous work fails to deal with these challenges. In this paper, we decompose the coupon allocation task into two subtasks: the user intent detection task and the allocation task. Accordingly, we propose a two-stage solution: at the first stage (detection stage), we put forward a novel Instantaneous Intent Detection Network (IIDN) which takes the user-coupon features as input and predicts user real-time intents; at the second stage (allocation stage), we model the allocation problem as a Multiple-Choice Knapsack Problem (MCKP) and provide a computational efficient allocation method using the intents predicted at the detection stage. We conduct extensive online and offline experiments and the results show the superiority of our proposed framework, which has brought great profits to the platform and continues to function online

Machine learning methods for predicting multiple variables

This thesis studies a category of machine learning problems where the aim is to construct models that predict multiple target variables based on a common set of input variables. We developed a complete software for learning from multi-label data and showed that it is useful as both a scientific as well as a practical tool. We also studied the problem of multi-target regression and proposed novel techniques that treat target variables as additional input variables. The proposed methods manage to successfully model dependencies in the output space, thus attaining significantly better accuracy than both the baseline approach as well as four state-of-the-art methods from the literature. Finally, the thesis focused at a popular multi-label classification problem, automated image annotation. We proposed new vectorized image representation methods that attain state-of-the-art accuracy while also being highly scalable.Η διατριβή μελετά μία κατηγορία προβλημάτων μηχανικής μάθησης στα οποία σκοπός είναι η κατασκευή μοντέλων πρόβλεψης πολλαπλών μεταβλητών-στόχων από ένα κοινό σύνολο μεταβλητών εισόδου. Στα πλαίσια της διατριβής αναπτύχθηκε ένα πλήρες λογισμικό μάθησης από δεδομένα πολλαπλών ετικετών και επιδείχθηκε η χρησιμότητά του τόσο ως ερευνητικό όσο και ως πρακτικό εργαλείο. Επίσης, μελετήθηκε το πρόβλημα της παλινδρόμησης πολλαπλών-στόχων και προτάθηκαν καινοτόμες μέθοδοι οι οποίες μεταχειρίζονται τις μεταβλητές-στόχους ως επιπρόσθετες μεταβλητές εισόδου. Οι προτεινόμενες μέθοδοι καταφέρνουν να μοντελοποιήσουν με επιτυχία τις εξαρτήσεις στον χώρο εξόδου, πετυχαίνοντας έτσι σημαντικά μεγαλύτερη ακρίβεια τόσο σε σχέση με τη βασική προσέγγιση όσο και σε σχέση με τέσσερις προηγμένες μεθόδους της βιβλιογραφίας. Τέλος, η διατριβή εστίασε σε ένα διαδεδομένο πρόβλημα ταξινόμησης πολλαπλών ετικετών, την αυτόματη επισήμανση εικόνων. Προτάθηκαν νέες μέθοδοι διανυσματικής αναπαράστασης των εικόνων οι οποίες πετυχαίνουν κορυφαίες επιδόσεις από άποψη ακρίβειας ενώ παράλληλα χαρακτηρίζονται από εξαιρετικές δυνατότητες κλιμάκωσης

Information Theoretic Multi-Target Feature Selection via Output Space Quantization

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms—the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents an algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classification and multivariate regression. We achieve this by combining two ideas—deriving low-order information theoretic approximations for the input space and using quantization algorithms for deriving low-dimensional approximations of the output space. Under the above framework we derive a novel criterion, Group-JMI-Rand, which captures various high-order target interactions. In an extensive experimental study we showed that our suggested criterion achieves competing performance against various other information theoretic feature selection criteria suggested in the literature

Improving Diversity in Image Search via Supervised Relevance Scoring

International audienceResults returned by commercial image search engines should include relevant and diversified depictions of queries in order to ensure good coverage of users' information needs. While relevance has drastically improved in recent years, diversity is still an open problem. In this paper we propose a reranking method that could be implemented on top of such engines in order to provide a better balance between relevance and diversity. Our method formulates the reranking problem as an optimization of a utility function that jointly considers relevance and diversity. Our main contribution is the replacement of the unsupervised definition of relevance that is commonly used in this formulation with a supervised classification model that strives to capture a query and application-specific notion of relevance. This model provides more accurate relevance scores that lead to significantly improved diversification performance. Furthermore, we propose a stacking-type ensemble learning approach that allows combining multiple features in a principled way when computing the relevance of an image. An empirical evaluation carried out on the datasets of the MediaEval 2013 and 2014 "Retrieving Diverse Social Images" (RDSI) benchmarks confirms the superior performance of the proposed method compared to other participating systems as well as a stateof-the-art, unsupervised reranking method