The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. 1 1.

this article, we review previous research in recommender systems to identify frequently used approaches and representations. Four recommendation approaches were examined: knowledge engineering, collaborative filtering, a content-based approach, and a hybrid approach. Different recommendation approaches can be implemented using different analytical methods. Commonly used methods are neighborhood formation, association rule mining, machine learning techniques, etc

In this paper, we present new extensions to traditional approaches to recommender systems by making recommender systems support data warehousing capabilities. In particular, we propose recommender systems to work in multidimensional settings as opposed to the traditional twodimensional user/item environments. We also propose recommender systems to support rich profiling and OLAP capabilities. 1

Recommender systems recommend products to customers based on ratings or past customer behavior. Without any information about attributes of the products or customers involved, the problem has been tackled most successfully by a nearest neighbor method called collaborative filtering in the context, while additional efforts invested in building classification models did not pay off and did not increase the quality. Therefore, classification methods have mainly been used in conjunction with product or customer attributes. Starting from a view on the plain recommendation task without attributes as a multi-class classification problem, we investigate two particularities, its autocorrelation structure as well as the absence of re-occurring items (repeat buying). We adapt the standard generic reductions 1-vs-rest and 1-vs-1 of multi-class problems to a set of binary classification problems to these particularities and thereby provide a generic compound classifier for recommender systems. We evaluate a particular specialization thereof using linear support vector machines as member classifiers on MovieLens data and show that it outperforms state-ofthe-art methods, i.e., item-based collaborative filtering. 1.

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers ’ and vendors ’ needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm’s products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplace: • A shift in focus in many organizations, towards increasing the share of requirements among their current customers rather than fighting for new customers. • An explosion in data acquired about customers, through the integration of internal databases and acquisition of external syndicated data. • Computing power is increasing exponentially. • Software and tools are being developed to exploit these data and computers, bringing the analytical tools to the decision maker, rather than restricting their access to analysts. In spite of this growth in marketing practice, CRM research in academia remains nascent. This paper provides a framework for CRM research and describes recent advances as well as key research opportunities. See

We look at one of the informational novelties introduced by the existence of internet market, namely "the recommender systems". A recommender system is a system employed by some internet sellers, which collects data from all previous customers about their experiences and makes inferences from this data to recommend a product to an active customer. The recommender system can also be interpreted as a peer-to-peer system where the seller provides a platform for the buyers to share their experiences. We interpret the role of a recommender system as reducing uncertainty for the customers, which creates some additonal surplus to be distributed between the customers and sellers employing such systems. We differentiate the customers with respect to the extremity of their preferences, which also implies different valuations for decreased uncertainty. We show that an internet seller employing such as system can extract a non-negligable share of this surplus from the customers through higher prices in the presence of a competitive fringe without recommender systems. However optimal pricing by the seller with the system leads to a less than full market share, since the seller nds it optimal to leave out the buyers with moderate taste to the fringe. Thus the optimal pricing mechanism does not employ the recommender system at the efficient level, in other words there is under utilization. We also nd that the overall under-utilization might entail over-utilization of the system for some products and under-utilization for others.

We apply random graph modeling methodology to analyze bipartite consumer-product graphs that represent sales transactions to better understand consumer purchase behavior in e-commerce settings. Based on two real-world e-commerce data sets, we found that such graphs demonstrate topological features that deviate significantly from theoretical predictions based on standard random graph models. In particular, we observed consistently larger-than-expected average path lengths and a greater-than-expected tendency to cluster. Such deviations suggest that the consumers ’ product choices are not random even with the consumer and product attributes hidden. Our findings provide justification for a large family of collaborative filtering-based recommendation algorithms that make product recommendations based only on previous sales transactions. By analyzing the simulated consumer-product graphs generated by models that embed two representative recommendation algorithms, we found that these recommendation algorithm-induced graphs generally provided a better match with the real-world consumer-product graphs than purely random graphs. However, consistent deviations in topological features remained. These findings motivated the development of a new recommendation algorithm based on graph partitioning, which aims to achieve high clustering coefficients similar to those observed in the real-world e-commerce data sets. We show empirically that this algorithm significantly outperforms representative collaborative filtering algorithms in situations where the observed clustering coefficients

The paper presents a survey of the field of recommender systems and describes current recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. The paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities. These extensions include, among others, improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.

Daily deals sites such as Groupon offer deeply discounted goods and services to tens of millions of customers through geographically targeted daily e-mail marketing campaigns. In our prior work we observed that a negative side effect for merchants selling Groupons is that, on average, their Yelp ratings decline significantly. However, this previous work was primarily observational, rather than explanatory. In this work, we rigorously consider and evaluate various hypotheses about underlying consumer and merchant behavior in order to understand this phenomenon, which we dub the Groupon effect. We use statistical analysis and mathematical modeling, leveraging a dataset we collected spanning tens of thousands of daily deals and over 7 million Yelp reviews. We investigate hypotheses such as whether Groupon subscribers are more critical than their peers, whether Groupon users are experimenting with services and merchants outside their usual sphere, or whether some fraction of Groupon merchants provide significantly worse service to customers using Groupons. We suggest an additional novel hypothesis: reviews from Groupon users are lower on average because such reviews correspond to real, unbiased customers, while the body of reviews on Yelp contain some fraction of reviews from biased or even potentially fake sources. Although our focus is quite specific, our work provides broader insights into both consumer and merchant behavior within the daily deals marketplace.

As firms move toward a more disciplined approach to e-business strategic planning, managers are seeking metrics that will help them analyze the success of their e-business investments. Likewise, researchers require metrics to build analytical models of the impact of managerial strategy on firm performance and to validate empirical field research on specific managerial tactics. In this paper, we develop a comprehensive framework for identifying e-business applications associated with activities upstream in the value chain, that complements an existing framework for applications further down the value chain. We propose that the real value proposition in e-business applications can be found in functionality interaction where one application enables the successful functionality in another application. The framework provides a methodology for mapping e-business applications within the proposed frameworks, which then can be used to generate three different types of metrics that should be considered for evaluating e-business strategic initiatives. Further, a classification of e-businesses provides the basis for selecting those metrics that are important to the strategic thrusts of the organization. The methodology allows the e-business strategist to map the organization’s e-business objectives into a coherent, easily understood visual representation. The framework is based on an extensive literature review of several reference disciplines. 2