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Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems.

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, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that rely on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations.

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 several cross domain sequential recommendation models have been proposed to leverage information from a source domain to improve CTR predictions in a target domain, they did not take into account bidirectional latent relations of user preferences across source-target domain pairs. As such, they cannot provide enhanced cross-domain CTR predictions for both domains simultaneously. In this paper, we propose a novel approach to cross-domain sequential recommendations based on the dual learning mechanism that simultaneously transfers information between two related domains in an iterative manner until the learning process stabilizes. In particular, the proposed Dual Attentive Sequential Learning (DASL) model consists of two novel components Dual Embedding and Dual Attention, which jointly establish the two-stage learning process: we first construct dual latent embeddings that extract user preferences in both domains simultaneously, and subsequently provide cross-domain recommendations by matching the extracted latent embeddings with candidate items through dual-attention learning mechanism. We conduct extensive offline experiments on three real-world datasets to demonstrate the superiority of our proposed model, which significantly and consistently outperforms several state-of-the-art baselines across all experimental settings. We also conduct an online A/B test at a major video streaming platform Alibaba-Youku, where our proposed model significantly improves business performance over the latest production system in the company., Accepted to KDD21

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 recommendation tasks, thus failing to take into account bidirectional latent relations between users and items and achieve optimal recommendation performance. To address these issues, in this paper we propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Furthermore, we combine the dual learning method with the metric learning approach, which allows us to significantly reduce the required common user overlap across the two domains and leads to even better cross-domain recommendation performance. We test the proposed model on two large-scale industrial datasets and six domain pairs, demonstrating that it consistently and significantly outperforms all the state-of-the-art baselines. We also show that the proposed model works well with very few overlap users to obtain satisfying recommendation performance comparable to the state-of-the-art baselines that use many overlap users., Comment: Accepted to IEEE TKDE. arXiv admin note: text overlap with arXiv:1910.05189

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 surprising them by presenting "fresh" and previously unexplored items to the users. In this paper, we describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process by providing multi-cluster modeling of user interests in the latent space and personalized unexpectedness via the self-attention mechanism and via selection of an appropriate unexpected activation function. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches in terms of both accuracy and unexpectedness measures. In addition, we conduct an online A/B test at a major video platform Alibaba-Youku, where our model achieves over 3\% increase in the average video view per user metric. The proposed model is in the process of being deployed by the company., Comment: Accepted to RecSys20

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizing patients by severity of the disease. In this article we adopted an approach based on using an ensemble of deep convolutional neural networks for segmentation of slices of lung CT scans. Using our models, we are able to segment the lesions, evaluate patients’ dynamics, estimate relative volume of lungs affected by lesions, and evaluate the lung damage stage. Our models were trained on data from different medical centers. We compared predictions of our models with those of six experienced radiologists, and our segmentation model outperformed most of them. On the task of classification of disease severity, our model outperformed all the radiologists.

Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficult and time-consuming task for the doctor who analyzes them because every heartbeat needs to be classified, thus requiring highly accurate methods for automatic interpretation. In this paper, we present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with encoderdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage in gradient boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features and further increases performance of our approach. As a part of this work we acquired 5095 Holter recordings of patients annotated by an experienced cardiologist. A committee of three cardiologists served as a ground truth annotators for the 291 examples in the test set. We show that the proposed method outperforms the selected baselines, including two commercial-grade software packages and some methods previously published in the literature., Accepted for publication on BIOSIGNALS 2021

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 competitive performance with respect to the current state-of-the-art. We additionally explore the effects of space curvature on the quality of hyperbolic models and propose an efficient data-driven method for estimating its optimal value., Accepted at ACM RecSys 2020; 7 pages

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 expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures in order to improve unexpectedness performance. Contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this paper., Comment: Accepted at ACM TIST

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 features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective domains. We test the proposed method on a large dataset containing three domains of movies, book and music items and demonstrate that it consistently and significantly outperforms several state-of-the-art baselines and also classical transfer learning approaches., Accepted to WSDM 2020

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 and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures., Comment: Accepted to RecSys19'

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 European bank. We also conducted a pilot study on loan applicants of the bank, and the study produced significant financial gains for the organization. In addition, our method has several other advantages described in the paper that are very significant for the bank., Comment: KDD 2019

The future success of these systems depends on more than a Netflix challenge.

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 customers’ trust, sales, and other crucial business-related performance measures. To do this, we delivered content-based and context-aware recommendations through a live controlled experiment with real customers of a commercial European online publisher. We measured the recommendations’ accuracy and diversification, how much customers spent purchasing products during the experiment, the quantity and price of their purchases, and the customers’ level of trust. We show that collecting and using contextual information in recommendations affects business-related performance measures, such as company sales, by improving the accuracy and diversification of recommendations, which in turn improves trust and, ultimately, business performance results.

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. We test its performance on the several real-world datasets, where our model significantly outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization and human evaluations. We also empirically show that the generated reviews could not be easily distinguished from the organically produced reviews and that they follow the same statistical linguistics laws., Accepted to EMNLP 2019

Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.

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 consequence, the companies deploying RSes in their business applications face the decision of how to generate and deliver personalized recommendations to their users by choosing among many options. The problem has been largely treated from the machine learning performance perspective because there is relatively little research done from the business perspective. The decision of what kind of recommender engines should be used in a personalization application, given certain business conditions, has a strategic value because it affects the way customers perceive the company with respect to its competitors. Choosing the wrong way to personalize recommendations may not only require the redesign of the information systems but also to rebuild the relationships with customers and even the entire brand strategic positioning. The research issues addressed by this paper are (i) which recommendation strategies a company can deploy to generate and deliver recommendations to users, and (ii) which specific strategies should be used depending on the current business conditions. We propose taxonomy based on a literature analysis and a framework to associate each strategy with a certain setting. The proposed framework is empirically supported by four case studies.

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 rank all unseen items based on predicted interest? In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task or need. The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

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, by utilizing vehicle GPS data. Specifically, we formulate a new Route Recommendation with Relaxed Assumptions (RR-RA) problem, the goal of which is to recommend a sequence of locations to a driver based on his current location in order to maximize his business success. To make our recommendation practical and scalable for real practice, we need to produce recommendation results in a timely fashion once a request emerges. Therefore, we propose an efficient algorithm to efficiently generate recommendations. Furthermore, we identify and address a destination-oriented route recommendation (DORR) problem. Without solving DORR problem, RR-RA alone does not work well in practice because drivers may encounter the destination constraint on a daily basis. We develop a dedicated and efficient algorithm for solving DORR problem. The package of solutions for both RR-RA and DORR problems provide a comprehensive approach for route recommendations to drivers. We evaluate our methods using both real-world GPS data and synthetic data, and demonstrate the effectiveness and efficiency of proposed methods with different evaluation metrics.

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 underexplored field that has many open and interesting problems important to the industry and academia. This paper reviews some of the key aspects of CRM, describes certain problems and promising research directions in the field, and discusses how Web mining can contribute to solving these problems.

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 query language REQUEST that allows users to customize recommendations by formulating them in the ways satisfying personalized needs of the users. REQUEST is based on the multidimensional model of recommender systems that supports additional contextual dimensions besides traditional User and Item dimensions and also OLAP-type aggregation and filtering capabilities. This paper also presents the recommendation algebra RA, shows how REQUEST recommendations can be mapped into this algebra, and analyzes the expressive power of the query language and the algebra. This paper also shows how users can customize their recommendations using REQUEST queries through a series of examples.

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 customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization.

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 has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.

It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro-targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization.

Data analysts and naive users alike in information-intensive organizations need automated ways to build, analyze, and maintain very large collections of data mining models.

The need to validate large amounts of data with the help of the domain expert arises naturally in many data-intensive applications, including data mining, data stream, and database-related applications. This paper presents a general validation approach that generalizes different expert-driven validation methods developed for specialized validation problems. In particular, we model the validation process as a sequence of validation operators, explore various properties of such sequences, and present theoretical results that provide for better understanding of the validation process. We also address the problem of selecting the best validation sequence among the class of equivalent sequence permutations. We demonstrate that this optimization problem is NP-hard and present two heuristic algorithms for improving validation sequences.

There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a study and compared the predictive performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings, such as multiple segmentation levels, multiple real-world marketing data sets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our experiments show that both 1-to-1 and segmentation approaches significantly outperform aggregate modeling. Reaffirming anecdotal evidence of the benefits of 1-to-1 marketing, our experiments show that the 1-to-1 approach also dominates the segmentation approach for the frequently transacting customers. However, our experiments also show that segmentation models taken at the best granularity levels dominate 1-to-1 models when modeling customers with little transactional data using effective clustering methods. In addition, the peak performance of segmentation models are reached at the finest granularity levels, skewed towards the 1-to-1 case. This finding adds support for the microsegmentation approach and suggests that 1-to-1 marketing may not always be the best solution

A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we present new methods for discovering a minimal set of unexpected patterns by combining the two, independent concepts of minimality and unexpectedness, both of which have been well-studied in the KDD literature. We demonstrate the strengths of this approach experimentally using a case study in a marketing domain.

By leveraging customer reactions to personalized products and services, companies continuously improve their personalization processes through an iterative feedback loop resulting in the `virtuous cycle' of personalization.

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data-mining (DM) or optimization methods, but has not combined the two approaches. By leveraging the strengths of both approaches, the eCRM problems of customer analysis, customer interactions, and the optimization of performance metrics (such as the lifetime value of a customer on the Web) can be better analyzed. In particular, many eCRM problems have been traditionally addressed using DM methods. There are opportunities for optimization to improve these methods, and this paper describes these opportunities. Further, an online appendix (mansci.pubs.informs.org/ecompanion.html) describes how DM methods can help optimization-based approaches. More generally, this paper argues that the reformulation of eCRM problems within this new framework of analysis can result in more powerful analytical approaches.

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 large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system., 6 pages, 2 figures in SIGIR 2015, SIRIP Symposium on IR in Practice

This paper presents an architecture of the e-Butler service, i.e. a personalized consumer-centric infomediary delivering a broad range of shopping-related services to consumers. In order to provide these services effectively, e-Butler should be "intelligent". This paper describes how a certain level of intelligence can be achieved through the utilization of a broad range of technologies, including data mining, personalization, profiling, recommender systems, data warehousing, Web proxy servers, data monitoring and XML-based technologies, that are integrated into a unique architecture of e-Butler.

In prior work, we provided methods that generate unexpected patterns with respect to managerial intuition by eliciting managers' beliefs about the domain and using these beliefs to seed the search for unexpected patterns in data. Unexpected patterns discovered in this manner represent contradictions or "holes" in domain knowledge which need to be resolved. Given a belief and a set of unexpected patterns, the motivation behind knowledge refinement is that the belief can be made stronger by refining the belief based on the discovered patterns. In this paper we address the problem of incorporating the discovered contradictions into the belief system based on a formal logic approach. Specifically, we present a framework for refinement based on a generic knowledge refinement strategy, describe abstract properties of refinement algorithms that can be used to compare specific instantiations and then describe and compare two specific refinement algorithms based on this framework.

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 special issue presents some representative research in social and economic computing from several perspectives.

This paper describes 1:1 Pro system which constructs personal profiles based on customers' transactional histories. The system uses data mining techniques to discover a set of rules describing customers' behavior and supports human experts in validating the rules.

In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating “good” rules from the “bad.” This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis in a reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules at a time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application.

Organizations are taking advantage of “data-mining” techniques to leverage the vast amounts of data captured as they process routine transactions. Data mining is the process of discovering hidden structure or patterns in data. However, several of the pattern discovery methods in data-mining systems have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverage to a full extent valuable prior domain knowledge that managers have. This research addresses these drawbacks by developing ways to generate interesting patterns by incorporating managers' prior knowledge in the process of searching for patterns in data. Specifically, we focus on providing methods that generate unexpected patterns with respect to managerial intuition by eliciting managers' beliefs about the domain and using these beliefs to seed the search for unexpected patterns in data. Our approach should lead to the development of decision-support systems that provide managers with more relevant patterns from data and aid in effective decision making.

The continued growth and proliferation of e-commerce, online services, and Webbased information systems, has led to an explosion of available content and resources in digital form. As a result of user interactions with these resources, tremendous volumes of clickstream, transaction, and user data are collected by organizations in their daily operations. Mining and analyzing such data can help these organizations determine the life time value of clients, design cross marketing strategies across products and services, evaluate the effectiveness of targeted promotional campaigns, and thus provide more effective personalized content to visitors. Automatic personalization is a central technology used in user-adaptive systems to deliver dynamic content, such as textual information, links, advertisements, and product recommendations that are tailored to the needs and interests of a particular user or a segment of users. The process of personalization comprises iterative invocations of the personalization cycle consisting of the stages of understanding the user and building user profiles and models; delivering personalized offerings to the users based on this understanding; measuring impacts of these offerings on the users; and adjusting all the previous stages based on the measured feedback. Data mining is one of the key technologies used in various stages of this personalization process, especially in building user models and delivering personalized offerings to the users. Therefore, it is crucial to develop appropriate data mining methods fitting the corresponding personalization tasks well. This special issue of User Modeling and User-Adapted Interaction explores recent developments and applications of data mining and machine learning techniques in

Investigates the information needs of marketers on the WWW for consumer analysis purposes, and examines how these can be met. Begins by briefly highlighting the attractiveness of the Web as a medium for communication. Next, summarizes the bases for consumer analysis and segmentation in the context of the traditional marketing communication media. Based on this framework, examines consumer analysis needs in the context of the WWW medium and proposes analysis variables relevant to this medium. Finally, discusses how the information needs for consumer analysis may be met from the different information sources available to a marketer, including the Web logfiles which are generated as a result of tracking the interactions of visitors accessing information from a company’s Web site.

A Query-Driven Simulation (QDS) system Cassandra+ is described that allows end-users to ask temporal queries about outcomes of running simulation models written in various simulation languages. The architecture of Cassandra+, its query language SimQL, and the implementation of the system are described. It is argued that Cassandra+ provides a more effective information systems (IS) support for simulations than the more traditional approaches by letting the users ask questions of interest about simulation outcomes in a declarative, flexible, and interactive fashion. To demonstrate the usefulness and practicality of the QDS approach and Cassandra+ in particular to asking queries about simulation outcomes, we present a case study. In this case study, we show how Cassandra+ performs on an "industrial-strength" manufacturing application developed by the management consulting company of Booz, Allen & Hamilton.

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared over the past decade. It has long been known that these proposals have adopted one of two basic approaches to the incorporation of time into the extended relational model. Recent work formally contrasted the expressive power of these two approaches, termed temporally ungrouped and temporally grouped, and demonstrated that the temporally grouped models are more expressive. In the temporally ungrouped models, the temporal dimension is added through the addition of some number of distinguished attributes to the schema of each relation, and each tuple is “stamped” with temporal values for these attributes. By contrast, in temporally grouped models the temporal dimension is added to the types of values that serve as the domain of each ordinary attribute, and the application's schema is left intact. The recent appearance of TSQL2, a temporal extension to the SQL-92 standard based upon the temporally ungrouped paradigm, means that it is likely that commercial DBMS's will be extended to support time in this weaker way. Thus the distinction between these two approaches—and its impact on the day-to-day user of a DBMS—is of increasing relevance to the database practitioner and the database user community. In this paper we address this issue from the practical perspective of such a user. Through a series of example queries and updates, we illustrate the differences between these two approaches and demonstrate that the temporally grouped approach more adequately captures the semantics of historical data.

In this paper, the formalism of Relational Transition Systems (RTSes) is used to model data-intensive reactive systems, and four RTS models of reactive systems based on temporal logic programming, production systems, recurrence equations, and Petri nets are presented. The paper also describes different methods of comparison of the expressive powers of various RTSes in terms of the trajectories they can generate and carries out this comparison for the four RTS formalisms. It is shown that these formalisms have the same expressive power in the deterministic case. The paper also compares expressive powers of non-deterministic production systems and non-deterministic temporal logic programming systems. It is shown that, although the two formalisms are incomparable in the general case, their restricted versions are isomorphic to each other.

When decision makers want to know outcomes of business processes in their organizations, they often use simulations to do this. Traditionally, this is achieved by running simulations, collecting statistics and answering questions of interests to the decision makers based on the collected statistics. This paper describes an alternative approach to simulations, called the Query-Driven Simulations (QDS) approach, in which the user first asks the queries of interest and then, depending on the query asked, appropriate simulations are run to answer that query. The QDS approach empowers the end-user by making it easier, faster, and more reliable to ask ad-hoc questions about outcomes of business processes than what is done in traditional simulations. To substantiate this point, the paper describes the types of questions decision makers ask about outcomes of business processes and studies how easy it is to express these questions in terms of an SQL-like query language SimQL designed for Query-Driven Simulations. In addition, a case study of using the QDS approach in a manufacturing application developed by a major management consulting company is presented and the QDS approach is tested on that application.

One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures-those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures-those that also depend on the class of users who examine the pattern. The focus of the paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.

The issue of periodicity is generally understood to be a desirable property of temporal data that should be supported by temporal database models and their query languages. Nevertheless, there has so far not been any systematic examination of how to incorporate this concept into a temporal DBMS. In this paper we describe two concepts of periodicity, which we call strong periodicity and near periodicity, and discuss how they capture formally two of the intuitive meanings of this term. We formally compare the expressive power of these two concepts, relate them to existing temporal query languages, and show how they can be incorporated into temporal relational database query languages, such as the proposed temporal extension to SQL, in a clean and straightforward manner.

A high-level simulation language based on temporal logic is described. The language combines a large set of temporal tenses and a rich class of high-level modeling primitives. Also an implementation of the language interpreter is presented. Finally, a real-world case study is described that shows how a programmer can develop structured, reliable, and well-maintainable simulation programs using the language.

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations of these temporal models and into the extended relational algebra or calculus that they define. Because of these differences, it has been difficult to compare the proposed models and to make judgments as to which of them might in some sense be equivalent or even better . In this paper we define temporally grouped and temporally ungrouped historical data models and propose two notions of historical relational completeness , analogous to Codd's notion of relational completeness, one for each type of model. We show that the temporally ungrouped models are less expressive than the grouped models, but demonstrate a technique for extending the ungrouped models with a grouping mechanism to capture the additional semantic power of temporal grouping. For the ungrouped models, we define three different languages, a logic with explicit reference to time, a temporal logic, and a temporal algebra, and motivate our choice for the first of these as the basis for completeness for these models. For the grouped models, we define a many-sorted logic with variables over ordinary values, historical values, and times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped calculus extended with a grouping mechanism. We believe the classification of historical data models into grouped and ungrouped models provides a useful framework for the comparison of models in the literature, and furthermore, the exposition of equivalent languages for each type provides reasonable standards for common, and minimal, notions of historical relational completeness.