A Large-Scale, Hybrid Approach for Recommending Pages Based on Previous User Click Pattern and Content

Abstract

In a large-scale recommendation setting, item-based collaborative filtering is preferable due to the availability of huge number of users’ preference information and relative stability in item-item similarity. Item-based collaborative filtering only uses users’ items preference information to predict recommendation for targeted users. This process may not always be effective, if the amount of preference information available is very small. For this kind of problem, item-content based similarity plays important role in addition to item co-occurrence-based similarity. In this paper we propose and evaluate a Map-Reduce based, large-scale, hybrid collaborative algorithm to incorporate both the content similarity and co-occurrence similarity. To generate recommendation for users having more or less preference information the relative weights of the item-item content-based and co-occurrence-based similarities are user-dependently tuned. Our experimental results on Yahoo! Front Page “Today Module User Click Log” dataset shows that we are able to get significant average precision improvement using the proposed method for user-dependent parametric incorporation of the two similarity metrics compared to other recent cited work.