Most conventional recommendation systems are based on service-specific data repositories containing both user and item data. In this paper, we introduce an alternative approach called UPCV (Ubiquitous Personal Context Vectors) that inherently supports distributed computing and distributed data repositories. The principal idea is that each user-item interaction can update the data associated with both the user and the item. When updating, item data is made to slightly resemble user data and vice versa, leading to increasing similarity between them. Through interactions, similarity will spread from users to items, from items to users, making it possible to inherently provide user-item, item-item, item-user and user-user recommendations. The principle introduced in this paper can be used as a baseline for the design of different types of collaborative recommender systems. The main advantages of this method are that it requires no content analysis, preserves users’ privacy and supports scalability. The method was evaluated using data from 1575 book club members: the members were asked which books they had read and liked. The quantitative analysis indicates that the most promising results are obtained for active readers. However, even for less active readers and without content analysis, the recommendation list tends to be populated by the same authors and/or authors of the same genre that the readers have liked, leading to meaningful recommendations.
|Number of pages||7|
|Journal||Journal of Print and Media Technology Research|
|Publication status||Published - 2013|
|MoE publication type||A1 Journal article-refereed|
- collaborative filtering
- distributed computing
- cloud computing