Combining configuration and recommendation to enable an interactive guidance of product line configuration

This paper is interested in e-commerce for complex configurable products/systems. E-commerce makes a wide use of recommendation techniques to help customers identify relevant products or services in large collections of offers. One particular way to achieve this is to offer customers a panel of options among which they can select their preferred ones. A trend in the industry is to go a step further, beyond the selection of pre-defined products from a catalogue by handling products customization. The systems engineering community has shown that, based on product line engineering methods, techniques and tools, it is possible to produce customized products efficiently and at low cost. The problem is that there are usually so many products in a PL that it is impossible to specify all of them explicitly, and therefore traditional recommendation techniques cannot be simply applied. This paper proposes an approach that combines two complementary forms of guidance: configuration and recommendation, to help customers define their own products out of a product line specification. The proposed approach, called interactive configuration supports the combination by organizing the configuration process in a series of partial configurations where decisions are made by the recommendation. This paper illustrates this process by applying it to an example with the content based method for recommendation and the a priori configuration approach

Recommending Related Products Using Graph Neural Networks in Directed Graphs

Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem nontrivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30-160% in terms of HitRate and MRR for the node recommendation task.Comment: This work was accepted in ECML PKDD 202

B2c e-commerce adoption in inner cities: An evolutionary perspective

Internet makes it possible for consumers to shop without visiting a physical store. As online shopping is becoming more popular, this could have significant impact on in-store shopping. The extent to which consumers, producers and retailers make use of the Internet as a complementary channel or as a substitute for in-store shopping is fundamental for the way traditional retailing will be affected. It is only recently that geographers are becoming interested in the spatial consequences of this new form of commerce. From a traditional geographical perspective, one could expect that business-to-consumer (b2c) e-commerce could make physical shopping redundant, leading to a ‘death of distance’. There are, however, several factors that may limit this new form of commerce, such as logistical constraints (e.g., personal delivery of goods may be quite expensive), habits of people, and the need for social contact. The main goal of the paper is to draw some expectations concerning the relationship between b2c e-commerce and inner city retailing. Using new insights based on evolutionary economics, hypotheses will be developed concerning the impact of b2c e-commerce on consumers’ shopping behaviour, retailers’ store strategy, and the inner city retailing environment as a whole. We claim that habits may act as a constraint to change consumers’ shopping behaviour. In addition, routines can explain why retailers may be rather reluctant in exploiting this new channel of commerce, and why they are most likely to adopt rather conservative e-commerce strategies. We also explain how and why inner cities, as important retailing and consumption places, may affect the way actors deal with this new form of commerce. One may expect that especially in these localities, both stimulating and limiting factors of b2c e-commerce adoption are predominant, depending on the quality or the attractiveness of the inner cities, among other things.evolutionary economics, e-commerce, urban economics

A Configurable Matchmaking Framework for Electronic Marketplaces

E-marketplaces constitute a major enabler of B2B and B2C e-commerce activities. This paper proposes a framework for one of the central activities of e-marketplaces: matchmaking of trading intentions lodged by market participants. The framework identifies a core set of concepts and functions that are common to all types of marketplaces and can serve as the basis for describing the distinct styles of matchmaking employed within various market mechanisms. A prototype implementation of the framework based on Web services technology is presented, illustrating its ability to be dynamically configured to meet specific market needs and its potential to serve as a foundation for more fully fledged e-marketplace frameworks

Inferring Networks of Substitutable and Complementary Products

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.Comment: 12 pages, 6 figure

Carving out new business models in a small company through contextual ambidexterity: the case of a sustainable company

Business model innovation (BMI) and organizational ambidexterity have been pointed out as mechanisms for companies achieving sustainability. However, especially considering small and medium enterprises (SMEs), there is a lack of studies demonstrating how to combine these mechanisms. Tackling such a gap, this study seeks to understand how SMEs can ambidextrously manage BMI. Our aim is to provide a practical artifact, accessible to SMEs, to operationalize BMI through organizational ambidexterity. To this end, we conducted our study under the design science research to, first, build an artifact for operationalizing contextual ambidexterity for business model innovation. Then, we used an in-depth case study with a vegan fashion small e-commerce to evaluate the practical outcomes of the artifact. Our findings show that the company improves its business model while, at the same time, designs a new business model and monetizes it. Thus, our approach was able to take the first steps in the direction of operationalizing contextual ambidexterity for business model innovation in small and medium enterprises, democratizing the concept. We contribute to theory by connecting different literature strands and to practice by creating an artifact to assist managemen