A CONTEXT-AWARE RECOMMENDATION APPROACH TOWARDS LOGISTICS DISTRIBUTION

Recommender systems are playing an important role in logistics distribution, while conventional recommendation approaches of logistics distribution have the problem of lack of contextual information. In this research, we analyze the important contextual information relevant in logistics distribution and propose a recommendation approach combined with context awareness (RACCA). RACCA first calculates the initial values of user's preferences according to current user's historical rating in combination with other user's ratings and builds a 2-dimensional network. In addition, RACCA analyses the weights of preferences of each user using the 3-dimensional recommendation system of user-item context from the perspective of spatial distance. Finally, RACCA holds the view that content-based filtering as well as collaborative can filter recommendation to implement forecasts and recommendations of user preferences in logistics distribution. Results reflect that RACCA has the advantage to improve accuracy in logistics distribution forecast, with an improvement of 6.03%-12.59% over other six approaches. The RACCA integrates multidimensional contextual factors into the process of recommendations and thus can provide a more accurate description of user preferences as well as high quality recommendation services for logistics distribution.