Question and answer (Q&A) forums, as a way for seeking expertise on the Internet, have seen rapid growth in popularity in recent years. The expertise available on most such forums is voluntary, provided by individuals willing to invest their resources for no monetary remuneration. While these forums provide easy access to expertise, the expertise available is often lacking in quality and depth. Two major reasons for this are, the time investment required to participate in such forums, and the lack of a mechanism for identifying experts for specialized questions. We believe a Q&A recommender engine can ameliorate this problem significantly. The two primary contributions of this work are: a) a hierarchical Bayesian model based Q&A recommender, and b) a discussion of metrics to measure the performance of such a Q&A recommender. Two new metrics, responder load and questioner satisfaction, are suggested based on this discussion. These metrics are used to evaluate the performance of the recommender system on datasets harvested from the Yahoo! Answers website. / text

Yes / This special issue of the Journal of Retailing and Consumer Services aims to bring together a variety of disciplines and a scholarly community for the advancement of knowledge regarding practice and research related to digital and social media marketing. Accordingly, the special issue includes papers using a variety of theories, research methods, contexts, and conceptualizations to enrich our understanding of digital and social media marketing from consumer and practitioner perspectives. The thirteen articles accepted for inclusion in this special issue are summarised in this editorial introduction.

Comment l'accès au savoir peut-il être impacté par la technologie ? Il suffit d'observer le virage intenté par les outils de communication au début des années 2000 pour se rendre compte : convergence des médias, pratiques participatives et numérisation massive des données. Dans ce contexte, on imagine que l'accès au savoir tend à se démocratiser. En effet, les individus semblent se réapproprier les espaces de vie, en inversant le modèle de transmission top-down, qui va du producteur vers le consommateur, au profit de processus de transfert basés sur l'intelligence collective. Pourtant, on aurait tort de réduire cette réorganisation à un simple renversement du modèle. Car l'intelligence collective est encline à divers biais cognitifs et socio-cognitifs, amenant parfois vers des situations irrationnelles. Autrefois, on s’accommodait de ces mécaniques sociales aux conséquences limitées, aujourd'hui les savoirs numérisés constituent des ensembles massivement communiquant, donnant naissance à de nouvelles voies d'accès et à de nouveaux clivages. Pourquoi ce savoir qui n'a jamais été aussi massif et ouvert, se révèle-t-il si sélectif ? Je propose d'explorer ce paradoxe. L'enregistrement massif et constant de nos traces numériques et l'hyper-connexion des individus, participent à la construction de structures organisationnelles, où se retrouvent numérisées de manière complexe, une partie des dynamiques sociales. En formalisant de la sorte les voies navigables, ces structures organisationnelles façonnent nos trajectoires. Sur cette base, les informaticiens ont mis au point des algorithmes de parcours individualisés, ayant pour objectifs de prédire et de recommander. Ainsi, on propose d'automatiser l'accès au savoir. Se pose alors la question de la gouvernance des individus, dans un contexte où l'intelligence collective est soumise à l'infrastructure : enregistrement des traces, composition des structures organisationnelles et algorithmes de parcours. / How access to knowledge can be impacted by Information Technology? In the earlier 2000s, communication tools caused a significant turn : media convergence, participative practices and massive data. In this way, free access to knowledge might tend to be democratized. People seem to regain spaces, reversing traditional top-down model, going from producer to consumer, for the benefit of an horizontal model based on collective intelligence. However, it should not automatically be assumed that this leads to a simple model reversing. Collective intelligence is subject to cognitive biases, leading to potential irrational situations. Formerly, those social mechanisms had limited consequences. Nowadays, digital knowledge are massive communicating spaces, giving birth to new access paths and new cleavages. Why this massive and open knowledge, is actually so selective? I propose to explore this paradox. Massive and constant tracking of traces and individuals hyper-connection, these two facts help organizational structures design, where social dynamics are digitalized in a complex way. These structures formalize human trajectories. On this basis, computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is automatized. It can then be asked about people governance, in this context of infrastructure submission: recording traces, designing knowledge structure and automating algorithms.