Integrated Collaborative Filtering for Implicit Feedback Incorporating Covisitation

Hongmei LI  Xingchun DIAO  Jianjun CAO  Yuling SHANG  Yuntian FENG  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.7   pp.1530-1533
Publication Date: 2017/07/01
Publicized: 2017/04/17
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8020
Type of Manuscript: LETTER
Category: Data Engineering, Web Information Systems
collaborative filtering,  implicit feedback,  matrix factorization,  covisitation,  relative preference,  

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Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.