Timeliness in recommender systems

Due to the high efficiency in finding the most relevant online products for users from the information ocean, recommender systems have now been applied to many commercial web sites. Meanwhile, many recommendation algorithms have been developed to improve the recommendation accuracy and diversity. However, whether the recommended items are timely or not in these algorithms has not yet been well understood. To investigate this problem, we consider a temporal data division which divides the links to probe set and training set strictly according to the time stamp on links. We find that the recommendation accuracy of many algorithms are much lower in temporal data division than in the random data division.With a timeliness metric, we find that the low accuracy is caused by the tendency of these algorithms to recommend out-of-date items, which cannot be detected with the random data division. To solve this problem, we improve the considered recommendation algorithms with a timeliness factor. The resulting algorithms can strongly suppress the probability of recommending obsolete items. Meanwhile, the recommendation accuracy is substantially enhanced.