CAJ | 학술논문

协同过滤直接根据用户的行为记录去预测其可能喜欢的产品，是现今最为成功、应用最广泛的推荐方法。概率矩阵分解算法是一类重要的协同过滤方式。它通过学习低维的近似矩阵进行推荐，能够有效处理海量数据。然而，传统的概率矩阵分解方法往往忽略了用户(产品)之间的结构关系，影响推荐算法的效果。通过衡量用户(产品)之间的关系寻找相似的邻居用户(产品)，可以更准确地识别用户的个人兴趣，从而有效提高协同过滤推荐精度。为此，提出一种对用户(产品)间的时序行为建模的方法。基于该方法，可以发现对当前用户(产品)影响最大的邻居集合。进一步地，将该邻居集合成功融合到基于概率矩阵分解的协同过滤推荐算法中。在两个真实数据集上的验证结果表明，所提出的 SequentialMF 推荐算法与传统的使用社交网络信息与标签信息的推荐算法相比，能够更有效地预测用户实际评分，提升推荐精度。
협동과려직접근거용호적행위기록거예측기가능희환적산품，시현금최위성공、응용최엄범적추천방법。개솔구진분해산법시일류중요적협동과려방식。타통과학습저유적근사구진진행추천，능구유효처리해량수거。연이，전통적개솔구진분해방법왕왕홀략료용호(산품)지간적결구관계，영향추천산법적효과。통과형량용호(산품)지간적관계심조상사적린거용호(산품)，가이경준학지식별용호적개인흥취，종이유효제고협동과려추천정도。위차，제출일충대용호(산품)간적시서행위건모적방법。기우해방법，가이발현대당전용호(산품)영향최대적린거집합。진일보지，장해린거집합성공융합도기우개솔구진분해적협동과려추천산법중。재량개진실수거집상적험증결과표명，소제출적 SequentialMF 추천산법여전통적사용사교망락신식여표첨신식적추천산법상비，능구경유효지예측용호실제평분，제승추천정도。
Collaborative filtering, which makes personalized predictions by learning the historical behaviors of users, is widely used in recommender systems. The key to enhance the performance of collaborative filtering is to precisely learn the interests of the active users by exploiting the relationships among users and items. Though various works have targeted on this goal, few have noticed the sequential correlations among users and items. In this paper, a method is proposed to capture the sequential behaviors of users and items, which can help find the set of neighbors that are most influential to the given users (items). Furthermore, those influential neighbors are successfully applied into the recommendation process based on probabilistic matrix factorization. The extensive experiments on two real-world data sets demonstrate that the proposed SequentialMF algorithm can achieve more accurate rating predictions than the conventional methods using either social relations or tagging information.