Utskrift från Malmö universitets webbplats www.mah.se

Improving Search and Recommendation for e-Commerce

Contact person: Bengt J. Nilsson
Co-workers: Natalie Schlüter and Dimitris Paraschakis
Partner: Apptus Technologies AB i Lund
Funding: The Knowledge Foundation
Timeframe: 2014-01-01 -- 2015-12-31
Faculty/Department: Faculty of Technology and Society, Department of Computer Science
Webpage: http://iotap.mah.se/search-recommendation-ecommerce/

Investigating ‘cold-start’ product-database search and recommendation.

In e-commerce, it is of central importance to present the products users are looking for as soon as possible. Products that are not presented, even though a consumer would be interested in them, result in unexploited revenue for the company. More importantly, products that are presented – even though the consumer is not interested in them – lead to eroded trust among customers.

Of particular difficulty are the so-called dynamicity and cold-start problems. Cold-start denotes the situation where there is a lack of information with respect to the user or the product’s popularity. State-of-the-art research into recommendation shows that accounting for dynamicity with respect to drifting popularity and user interests leads to improvements in system performance.

This research project outlines real-world investigation of the vital areas of coldstart product database search and recommendation. It turns out that the problem that e-commerce sites face in the cold-start can be modelled by a well-known two-player game called the Contextual Bandit problem, which asks for the best strategy of product retrieval that the site can make given the user’s query

Senast uppdaterad av Magnus Jando