Goal-driven Collaborative Filtering

Recommender systems aim to identify interesting items (e.g. movies, books, websites) for a given user, based on their previously expressed preferences. As recommender systems grow in popularity, a notable divergence emerges between research practices and the reality of deployed systems: when recommendation algorithms are designed, they are evaluated in a relatively static context, mainly concerned about a predefined error measure. This approach disregards the fact that a recommender system exists in an environment where there are a number of factors that the system needs to satisfy, some of these factors are dynamic and can only be tackled over time. Thus, this thesis intends to study recommender systems from a goal-oriented point of view, where we define the recommendation goals, their associated measures and build the system accordingly. We first start with the argument that a single fixed measure, which is used to evaluate the system’s performance, might not be able to capture the multidimensional quality of a recommender system. Different contexts require different performance measures. We propose a unified error minimisation framework that flexibly covers various (directional) risk preferences. We then extend this by simultaneously optimising multiple goals, i.e., not only considering the predicted preference scores (e.g. ratings) but also dealing with additional operational or resource related requirements such as the availability, profitability or usefulness of a recommended item. We demonstrate multiple objectives through another example where a number of requirements, namely, diversity, novelty and serendipity are optimised simultaneously. At the end of the thesis, we deal with time-dependent goals. To achieve complex goals such as keeping the recommender model up-to-date over time, we consider a number of external requirements. Generally, these requirements arise from the physical nature of the system, such as available computational resources or available storage space. Modelling such a system over time requires describing the system dynamics as a combination of the underlying recommender model and its users’ behaviour. We propose to solve this problem by applying the principles of Modern Control Theory to construct and maintain a stable and robust recommender system for dynamically evolving environments. The conducted experiments on real datasets demonstrate that all the proposed approaches are able to cope with multiple objectives in various settings. These approaches offer solutions to a variety of scenarios that recommender systems might face

Detection and Measurement of Sales Cannibalization in Information Technology Markets

Characteristic features of Information Technology (IT), such as its intrinsic modularity and distinctive cost structure, incentivize IT vendors to implement growth strategies based on launching variants of a basic offering. These variants are by design substitutable to some degree and may contend for the same customers instead of winning new ones from competitors or from an expansion of the market. They may thus generate intra-organizational sales diversion – i.e., sales cannibalization. The occurrence of cannibalization between two offerings must be verified (the detection problem) and quantified (the measurement problem), before the offering with cannibalistic potential is introduced into the market (ex-ante estimation) and/or afterwards (ex-post estimation). In IT markets, both detection and measurement of cannibalization are challenging. The dynamics of technological innovation featured in these markets may namely alter, hide, or confound cannibalization effects. To address these research problems, we elaborated novel methodologies for the detection and measurement of cannibalization in IT markets and applied them to four exemplary case studies. We employed both quantitative and qualitative methodologies, thus implementing a mixed-method multi- case research design. The first case study focuses on product cannibalization in the context of continuous product innovation. We investigated demand interrelationships among Apple handheld devices by means of econometric models with exogenous structural breaks (i.e., whose date of occurrence is given a priori). In particular, we estimated how sales of the iPod line of portable music players were affected by new-product launches within the iPod line itself and by the introduction of iPhone smartphones and iPad tablets. We could find evidence of expansion in total line revenues, driven by iPod line extensions, and inter- categorical cannibalization, due to iPhones and iPads Mini. The second empirical application tackles platform cannibalization, when a platform provider becomes complementor of an innovative third party platform thus competing with its own proprietary one. We ascertained whether the diffusion of GPS-enabled smartphones and navigation apps affected sales of portable navigation devices. Using a unit-root test with endogenous breaks (i.e., whose date of occurrence is estimated), we identified a negative shift in the sales of the two leaders in the navigation market and dated it at the third quarter of 2008, when the iOS and Android mobile ecosystems were introduced. Later launches of their own navigation apps did not significantly affect these manufacturers’ sales further. The third case study addresses channel cannibalization. We explored the channel adoption decision of organizational buyers of business software applications, in light of the rising popularity of online sales channels in consumer markets. We constructed a qualitative channel adoption model which takes into account the relevant drivers and barriers of channel adoption, their interdependences, and the buying process phases. Our findings suggest that, in the enterprise software market, online channels will not cannibalize offline ones unless some typical characteristics of enterprise software applications change. The fourth case study deals with business model cannibalization – the organizational decision to cannibalize an existent business model for a more innovative one. We examined the transition of two enterprise software vendors from on-premise to on-demand software delivery. Relying on a mixed- method research approach, built on the quantitative and qualitative methodologies from the previous case studies, we identified the transition milestones and assessed their impact on financial performances. The cannibalization between on-premise and on-demand is also the scenario for an illustrative simulation study of the cannibalization