Overcoming data sparsity

Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased items account for 80% of the data. Therefore, the data is sparse, skewed and demonstrates a long tail. Attempts to incorporate the data from the long tail, so far have proved difficult and current Unilever recommendation systems do not incorporate the information about infrequently purchased items. At the same time, these items are more indicative of customers' preferences and Unilever would like to make recommendations from/about these items, i.e. give a rank ordering of available products in real time. Study Group suggested to use the approach of bipartite networks to construct a similarity matrix that would allow the recommendation scores for different products to be computed. Given a current basket and a customer ID, this approach gives recommendation scores for each available item and recommends the item with the highest score that is not already in the basket. The similarity matrix can be computed offline, while recommendation score calculations can be performed live. This report contains the summary of Study Group findings together with the insights into properties of the similarity matrix and other related issues, such as recommendation for the data collection

Integrating knowledge from social and natural sciences for biodiversity management: The asymmetric information trap

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Bump formation in a binary attractor neural network

This paper investigates the conditions for the formation of local bumps in the activity of binary attractor neural networks with spatially dependent connectivity. We show that these formations are observed when asymmetry between the activity during the retrieval and learning is imposed. Analytical approximation for the order parameters is derived. The corresponding phase diagram shows a relatively large and stable region, where this effect is observed, although the critical storage and the information capacities drastically decrease inside that region. We demonstrate that the stability of the network, when starting from the bump formation, is larger than the stability when starting even from the whole pattern. Finally, we show a very good agreement between the analytical results and the simulations performed for different topologies of the network.Comment: about 14 page

Testing for Team Spirit - An Experimental Study

It is often suggested that team spirit counteracts free-riding. Testing for team spirit with field data is difficult, however, due to an inherent identification problem. In this paper test for team spirit experimentally. In a team work task we vary subjects' information about relative team performance while we leave unchanged the structure of explicit incentives. We find that subjects contribute more to their team's project when teams observe each others' performance. We attribute this result to the enhancement of team spirit caused by asymmetric peer effects between observing teams.team spirit, peer effects, organization of work, public goods experiments