Towards scalable and accurate item-oriented recommendations

Most recommenders research aims at personalized systems, which suggest items based on user profiles. However, in reality many systems deal with item-oriented recommendations. In such setups, given a single item of interest, the system needs to provide other related items, following patterns like "people who liked this also liked.". While item-oriented systems are central in their importance, they have been approached so far using very basic tools. We identify several hurdles faced by standard approaches to the item-oriented task. First, the sparseness of observed activities prevents establishing reliable similarity relations for many item pairs. Second, we address a scalability challenge at the retrieval stage present in many real-world systems: Given an item inventory, which may encompass millions of items, it is desired to identify the most related item pairs in a sub-quadratic time. This work addresses these two challenges, thereby improving both accuracy and scalability of item-oriented recommenders. Additionally, we propose an empirical evaluation scheme for comparing the quality of different solutions with encouraging results.