Collaborative filtering is a major technique to make personalized recommendations about information items (movies, books, webpages etc) to individual users. In the literature, a common research objective is to predict unknown ratings of items for a user, on the condition that the user has explicitly rated a certain amount of items. Nevertheless, in many practical situations, we may only have
evidence of user preferences, such as “playback times of a music file” or “visiting frequency of a web-site”. Most importantly, a more practical view of the recommendation task is to directly generate a top-
ranked list of items that the user is most likely to like.
In this paper, we take these two concerns into account. Item ranking in recommender systems is considered as a task highly related to document ranking in text retrieval. Firstly, two practical item scoring functions are derived by adopting the generative language modelling approach of text retrieval. Secondly, to address the uncertainty associated with the score estimation, we introduce a
model that penalizes the less reliable scores. Our experiments on real data sets demonstrate that significant performance gains have been achieved.