An Item-Targeted User Similarity Method for Data Service Recommendation
Memory-based methods for recommending data services predict the ratings of active users based on the information of other similar users or items, where the similarity algorithm always plays a key role. In many scenarios, the authors find that the similarity of two users always show different effectiveness when predicting different ratings. Normal similarity algorithms usually do not count the difference, since they originate from statistic and algebra fields and do not directly aim at recommendations. This paper proposes a novel method to amend the user similarity generated by a normal similarity algorithm to more accurately describe the effectiveness of the similarity on a targeted item.