A heuristic approach to identifying the specific household member for a given rating
Recently context-aware recommender systems have become one of the hottest topics in the domain of recommender systems. Household can be seen as an important context to user ratings, and therefore may play an important role in improving recommendation accuracy. In the paper, we propose a heuristic approach to identify which member of a household performs a specific rating by considering rating-related timestamp and household information. Firstly, we employ rating-related timestamp to select the appropriate user ratings in order to avoid introducing the outdated ratings, and then employ household information into the process of user rating prediction. Next, we design a heuristic approach to calculate the probabilities that a given item is rated by different members in a household based on the above prediction ratings. Finally, in order to enhance probability estimation accuracy, we analyze difference influences of different household members and introduce them into the final process of household member identification. We perform experimental comparisons of the above approach with some baselines on the Moviepilot dataset released for the Challenge on Context-Aware Movie Recommendation (CAMRa2011), and also analyze the results.