Alexander Tuzhilin

New York University | NYU · Department of Information, Operations and Management Sciences

Although building a 360-degree comprehensive view of a customer has been a long-standing goal in marketing, this challenge has not been successfully addressed in many marketing applications because fractured customer data stored across different “silos” are hard to integrate under “one roof” for several reasons. Instead of integrating customer data...

Static user and item features constitute important information to be taken into account in the recommendation process. However, as these features are usually sparse and of large-vocabulary, existing deep learning-based methods typically construct large tables of high-dimensional feature embeddings, which is inefficient in terms of memory storage an...

Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their structures are known in an application. Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual var...

Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations accordingly. Meanwhile, the heterogeneity of user behaviors is usually not well studied and captured during the recommendation process, where us...

In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers’ utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs...

In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review...

We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while...

Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. To address the filter bubble problem, unexpected recommendations have been proposed to recommend items significantly deviating from user's prior expectations and thus surp...

Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple category domains. Therefore, it has great potential to improve click-through-rate prediction performance in online commerce platforms having many domains of products. While seve...

Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. In addition, they did not consider the duality structure of cross-domain...

Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. In addition, they did not consider the duality structure of cross-domain...

In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs...

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increa...

In this paper, we propose hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-...

We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build our solution using only a single hidden layer. Remarkably, even with such a minimalistic approach, we not only outperform the Euclidean counterpart but also achieve a comp...

Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expe...

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in vario...

In this paper we consider a challenging problem of learning discriminative vector representations for event sequences generated by real-world users. Vector representations map behavioral client raw data to the low-dimensional fixed-length vectors in the latent space. We propose a novel method of learning those vector embeddings based on metric lear...

In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large Europ...

Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item...

In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information. RevGAN utilizes the combination of three novel components, including self-attentive recursive autoencoders, conditional discriminators, and personalized decoders...

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), man...

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or r...

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users...

While the initial goal of recommender systems (RSes) was to reduce the information overload for Internet users and make the information retrieval more efficient, they have become a crucial strategic tool for companies in the online markets. According to this evolution, research on RSes has produced a wide variety of approaches and algorithms. As a...

The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations, which are essential to recommend a sequence of locations to an individual user on the move. In this paper, we study a particular case of mobile recommendations, route recommendations to drivers,...

In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large Europ...

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users...

Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations when recommending items. Previously proposed approaches model user expectations in the feature space, making them limited to the items that the user has visited or expecte...

We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of model-agnostic distillation, such as an ability to focus on the particula...

We study the problem of providing recommendations to students that help them in their studies. To address this problem, we present an approach of providing recommendations of remedial learning materials to students that fill the gaps in their knowledge of the subject in the courses they take. According to this method, we first identify gaps in the...

Recommendation algorithms for ratings prediction and item ranking have steadily matured during the past decade. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background n...

In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also o...

This article presents a novel approach to detecting emergency events, such as power outages, that utilizes social media users as “social sensors” for virtual detection of such events. The proposed new method is based on the analysis of the Twitter data that leads to the detection of Twitter discussions about these emergency events. The method descr...

Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this mult...

Information Management Systems that outsource part of the functionality to other (likely unknown) services need an effective way to communicate with these services, so that a mutually beneficial solution can be generated. This includes bargaining for their optimal customizations and the discovery of overlooked potential solutions. In this article,...

In most BPM systems (a.k.a. workflow systems), the data for process execution is scattered across databases for enterprise, auxiliary local data stores within the BPM systems, and even file systems (e.g., specification of process models). The interleaving nature of data management and BP execution and the lack of a coherent conceptual data model fo...

Most of the work on context-aware recommender systems has focused on demonstrating that the contextual information leads to more accurate recommendations. Little work has been done, however, on studying how much the contextual information affects the business performance. In this paper, we study how including context in recommendations affects cust...

Daily deal coupons have gained a prominent foothold on the web. The earliest and largest player is Groupon. Originally, Groupon deals were a mix of deals with a minimum requirement (MR) of coupon sales before a deal became effective and of deals without a minimum requirement (NMR). Eventually, Groupon stopped using MR deals. For Groupon and its ret...

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in larg...

A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles....

In this paper, we propose new metrics to accurately measure the concentration reinforcement of recommender systems and the enhancement of the "long tail". We also conduct a comparative analysis of various RS algorithms illustrating the usefulness of the proposed metrics.

Focusing on the problems of over-specialization and concen- tration bias, this paper presents a novel probabilistic method for recommending items in the neighborhood-based collab- orative filtering framework. For the probabilistic neighbor- hood selection phase, we use an efficient method for weighted sampling of k neighbors that takes into conside...

A user specifies one or more monitoring/probing rules on a client station. These rules specify user instructions for monitoring and probing conditions, events, and data on monitored sites on the Internet. The client station transfers the rules to the monitoring site, which also processes and interprets the rules. When the monitoring site processes...

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for improvement is the concept of unexpectedness. In this paper, we propose a model to improve user satisfaction by generating unexpected recommendat...

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data could be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because t...

This paper proposes a novel method for estimating unknown ratings and recommendation opportunities and illustrates the practical implementation of the proposed approach by presenting a certain variation of the classical k-NN method in neighborhood-based collaborative filtering systems using weighted percentiles. We conduct an empirical study showin...

Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-fil...

CARS 2012 builds upon the success of the three previous editions held in conjunction with the 3rd to 5th ACM Conferences on Recommender Systems from 2009 to 2011. The 1st CARS Workshop was held in New York, NY, USA, whereas Barcelona, Spain, was home of the 2nd CARS Workshop in 2010. In 2011, the 3rd CARS workshop was held in Chicago, IL, USA.

After a decade of successful development of new Web mining technologies, it is a good time to examine novel promising areas that will advance Web mining over the next decade. This paper argues that CRM is such an area that can benefit from and contribute to further advancements of the Web mining research. This is the case because CRM is an underexp...

Social and economic computing is a cross-disciplinary field focusing on the development of computing technologies that consider social and economic contexts. Social computing and economic computing not only share a number of computing technologies, they also benefit and fertilize each other in computational theories, models, and design. This specia...

Knowledge Management involves acquisition, enhancement and utilization of organizational knowledge. Given the enormous quantities of data stored in organizational data warehouses, it stands to reason that data mining approaches could contribute significantly to the Knowledge Management process at hand. However most current data mining methods are p...

Rating sparsity is a critical issue for collaborative filtering. For example, the well-known Netflix Movie rating data contain ratings of only about 1% user-item pairs. One way to address this rating sparsity problem is to develop more effective methods for training rating prediction models. To this end, in this paper, we introduce a collective tra...

CARS 2011 builds upon the success of the two previous editions held in conjunction with the 3rd and 4th ACM Conferences on Recommender Systems in 2009 and 2010. The first CARS Workshop was held in New York, NY, USA (2009), and Barcelona, Spain, was the home of the second CARS Workshop in 2010.

Despite the growing popularity of Context-Aware Recommender Systems (CARSs), only limited work has been done on how contextual recommendations affect the behavior of customers in real-life settings. In this paper, we study the effects of contextual recommendations on the purchasing behavior of customers and their trust in the provided recommendatio...

The article considers the (old) field of Knowledge Management (KM) and examines the factors that led to its decline over the last several years. It also argues that the demise of knowledge management is premature and that it is time to revisit the field and take it to the “next level” of academic research and business practice. Then the article pre...

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approac...

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the...

Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years. However, the types of recommendations available to the users in these recommender systems are typically determined by the vendor and therefore are not flexible. In this paper we address this problem by presenting the recommendation q...

This article describes an approach for incorporating externally specified aggregate ratings information into certain types of recommender systems, including two types of collaborating filtering and a hierarchical linear regression model. First, we present a framework for incorporating aggregate rating information and apply this framework to the afo...

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems,...

The field of recommender systems has reached an important milestone in Fall 2009 when the $1,000,000 Netflix Prize Competition has successfully completed its course. Since then, there has been an attempt made in the recommender systems community to access what has been accomplished and identify new promising research directions that the field can p...

CARS and CAMRa were organized under the Context-awareness in Recommendation Systems special event and gathered academic researchers as well as industrial practitioners in a workshop and challenge.

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption...

Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post- filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the performance of these methods to determine which of them is better than the others. This paper focuse...

Previous work on using external aggregate rating informa- tion showed that this information can be incorporated in several dierent types of recommender systems and improves their performance. In this paper, we propose a more general class of methods that combine external aggregate informa- tion with individual ratings in a novel way. Unlike the pre...

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the cust...

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their ...

Recent research showed that including context in customer behavior models improves predictive performance, especially when the unit of analysis is the single customer. Also segmentation has proved to improve the performance of predictive modeling. The research contribution of this work lies in studying interaction effects between segmentation and c...

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the cust...

Aggregate-level ratings have been studied in recommender systems and have been shown to improve predictions of ratings of individual items for individual users. Similarly, individual-level ratings have also been used for the estimation of aggregate-level ratings for groups of items and users. In this paper, we combine these approaches and present a...

This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (CF) methods. For a statistical model-based CF approach, we formally showed that this additional aggregated information provides more accurate recommendations of individual items to individual users. Fu...

The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications have been done before. In this paper we study how important the conte...

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are bas...

By now, online social networks have become an indispensable part of both online and offline lives of human beings. A large fraction of time spent online by a user is directly influence by the social networks to which he/she belongs. This calls for a deeper examination of social networks as large-scale dynamic objects that foster efficient person-pe...

Data analysts and inexperienced users in the IT industry require the automated ways to build, analyze, and maintain very large collections of the data mining models. Various data mining researchers have proposed data mining query languages and M-SQL to integrate the data mining and database management systems and extend SQL to include operators for...

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems,...