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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 significant improvement is the concept of unexpectedness . In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system - the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.

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 the tourist’s choice is affected directly by the travel costs, which includes both financial and time costs. To that end, in this article, we provide a focused study of cost-aware tour recommendation. Along this line, we first propose two ways to represent user cost preference. One way is to represent user cost preference by a two-dimensional vector. Another way is to consider the uncertainty about the cost that a user can afford and introduce a Gaussian prior to model user cost preference. With these two ways of representing user cost preference, we develop different cost-aware latent factor models by incorporating the cost information into the probabilistic matrix factorization (PMF) model, the logistic probabilistic matrix factorization (LPMF) model, and the maximum margin matrix factorization (MMMF) model, respectively. When applied to real-world travel tour data, all the cost-aware recommendation models consistently outperform existing latent factor models with a significant margin.

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-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable "best bet" when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.

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 aforementioned individual rating models. Then we formally show that this additional aggregate rating information provides more accurate recommendations of individual items to individual users. Further, we experimentally confirm this theoretical finding by demonstrating on several datasets that the aggregate rating information indeed leads to better predictions of unknown ratings. We also propose scalable methods for incorporating this aggregate information and test our approaches on large datasets. Finally, we demonstrate that the aggregate rating information can also be used as a solution to the cold start problem of recommender systems.

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.

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.

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.

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.

Focusing on the problems of over-specialization and concentration bias, this paper presents a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework. For the probabilistic neighborhood selection phase, we use an efficient method for weighted sampling of k neighbors that takes into consideration the similarity levels between the target user (or item) and the candidate neighbors. We conduct an empirical study showing that the proposed method increases the coverage, dispersion, and diversity reinforcement of recommendations by selecting diverse sets of representative neighbors. We also demonstrate that the proposed approach outperforms popular methods in terms of item prediction accuracy, utility-based ranking, and other popular measures, across various experimental settings. This performance improvement is in accordance with ensemble learning theory and the phenomenon of "hubness" in recommender systems.

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.

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

Despite the growth in the size and complexity of corporate data, the technology for analyzing it has not kept up with the advances in data collection, in that managers mostly need to rely on marketing research and information systems experts to generate the analysis and reports they need. We review some useful approaches here from the computer science and information systems fields for the analysis of large data sets, viz. good data organization and the use of flexible analysis tools, for making the analysis more tractable and user-friendly. These methods are increasingly being adopted by practitioners who are hard-pressed to generate business intelligence from large corporate databases. However, the benefits of these approaches may not be confined only to practitioners, and may apply to academic researchers working with large data sets, as well.

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 showing that the proposed method outperforms the standard user-based collaborative filtering approach by a wide margin in terms of item prediction accuracy and utility-based ranking metrics across various experimental settings. We also demonstrate that this performance improvement is not achieved at the expense of other popular performance measures, such as catalog coverage and aggregate diversity. The proposed approach can also be applied to other popular methods for rating estimation.

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 of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.

Previous work on using external aggregate rating information showed that this information can be incorporated in several different types of recommender systems and improves their performance. In this paper, we propose a more general class of methods that combine external aggregate information with individual ratings in a novel way. Unlike the previously proposed methods, one of the defining features of this approach is that it takes into the consideration not only the aggregate average ratings but also the variance of the aggregate distribution of ratings. The methods proposed in this paper estimate unknown ratings by finding an optimal linear combination of individual-level and aggregate-level rating estimators in a form of a hierarchical regression (HR) model that is grounded in the theory of statistics and machine learning.The proposed HR model is general enough so that the standard individual-level recommender systems and naive aggregate methods constitute special cases of this model.We show that for the general HR model, the presence of the aggregate variance, surprisingly, does not significantly improve estimation of unknown ratings vis-a-vis the case when only aggregate average ratings are considered.In the paper, we experimentally show that the optimal linear combination approach significantly dominates all other special cases, including the classical non-aggregated case and our previously studied aggregate methods, and therefore is the method of choice.

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. Furthermore, theoretical insights gained from the analysis of this model-based method suggested a way to incorporate aggregate information into the heuristic item-based CF method. Both the model-based and the heuristic item-based CF methods were empirically tested on several datasets, and the experiments uniformly confirmed that the aggregate rating information indeed improves CF recommendations. These results also show the power of theory by demonstrating how the insights gained from theoretical developments can shed light on proper selection of good heuristic methods. We also showed the way to introduce scalability and parallelization into the estimation procedure and reported the running time for steps of the estimation procedure for large datasets.

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 based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

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 contextual information. Several experiments were done on a data set coming from an e-commerce application.

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions.

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 significantly 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.

This paper compares customer segmentation, 1-to-1, and aggregate marketing approaches across a broad range of experimental settings, including multiple segmentation levels, marketing datasets, dependent variables, and different types of classifiers, segmentation techniques, and predictive measures. Our experimental results show that, overall, 1-to-1 modeling significantly outperforms the aggregate approach among high-volume customers and is never worse than aggregate approach among low-volume customers. Moreover, the best segmentation techniques tend to outperform 1-to-l modeling among low-volume customers.

In this paper we study market share rules, rules that have a certain market share statistic associated with them. Such rules are particularly relevant for decision making from a business perspective. Motivated by market share rules, in this paper we consider statistical quantitative rules (SQ rules) that are quantitative rules in which the RHS can be any statistic that is computed for the segment satisfying the LHS of the rule. Building on prior work, we present a statistical approach for learning all significant SQ rules, i.e., SQ rules for which a desired statistic lies outside a confidence interval computed for this rule. In particular we show how resampling techniques can be effectively used to learn significant rules. Since our method considers the significance of a large number of rules in parallel, it is susceptible to learning a certain number of "false" rules. To address this, we present a technique that can determine the number of significant SQ rules that can be expected by chance alone, and suggest that this number can be used to determine a "false discovery rate" for the learning procedure. We apply our methods to online consumer purchase data and report the results.

The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for the analysis of such data. In this paper, we propose to use association rule discovery methods for determining associations among expression levels of different genes. One of the main problems related to the discovery of these associations is the scalability issue. Microarrays usually contain very large numbers of genes that are sometimes measured in 10,000s. Therefore, analysis of such data can generate a very large number of associations that can often be measured in millions. The paper addresses this problem by presenting a method that enables biologists to evaluate these very large numbers of discovered association rules during the post-analysis stage of the data mining process. This is achieved by providing several rule evaluation operators, including rule grouping, filtering, browsing, and data inspection operators, that allow biologists to validate multiple individual gane regulation patterns at a time. By iteratively applying these operators, biologists can explore a significant part of all the initially generated rules in an acceptable period of time and thus answer biological questions that are of a particular interest to him or her. To validate our method, we tested our system on the microarray data pertaining to the studies of environmental hazards and their influence of gane expression processes. As a result, we managed to answer several questions that were of interest to the biologists that had collected this data.

In this paper, we present a new data-warehousing-based approach to recommender systems. In particular, we propose to extend traditional two-dimensional user/item recommender systems to support multiple dimensions, as well as comprehensive profiling and hierarchical aggregation (OLAP) capabilities. We also introduce a new recommendation query language RQL that can express complex recommendations taking into account the proposed extensions. We describe how these extensions are integrated into a framework that facilitates more flexible and comprehensive user interactions with recommender systems.

There has been much attention given recently to the task of finding interesting patterns in temporal databases. Since there are so many different approaches to the problem of discovering temporal patterns, we first present a characterization of different discovery tasks and then focus on one task of discovering interesting patterns of events in temporal sequences. Given an (infinite) temporal database or a sequence of events one can, in general, discover an infinite number of temporal patterns in this data. Therefore, it is important to specify some measure of interestingness for discovered patterns and then select only the patterns interesting according to this measure. We present a probabilistic measure of interestingness based on unexpectedness, whereby a pattern P is deemed interesting if the ratio of the actual number of occurrences of P exceeds the expected number of occurrences of P by some user defined threshold. We then make use of a subset of the propositional, linear temporal logic and present an efficient algorithm that discovers unexpected patterns in temporal data. Finally, we apply this algorithm to synthetic data, UNIX operating system calls, and Web logfiles and present the results of these experiments.