Language Models of Collaborative Filtering

Abstract. 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 implicit 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-N 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. Second...

• Type: Conference
• Year: 2009
• Where: AIRS
• Authors: Jun Wang
• Keywords: AIRS 2009, Common Research Objective, Information Retrieval, Item Scoring Functions, Text Retrieval