Title
A New Temporal Recommendation System Based On Users' Similarity Prediction
Abstract
Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar users to provide suggestions for a target user. Users' preferences often vary over time and many traditional collaborative filtering algorithms fail to consider this important issue. In this paper, a novel recommendation method is proposed based on predicting similarity between users in the future and forecasting their similarity trends over time. The proposed method uses the sequence of users' ratings to predict the similarities between users in the future and use the predicted similarities instead of the original ones to detect users' neighbours. Experimental results on benchmark datasets show that the proposed method significantly outperforms classical and state-of-the-art recommendation methods.
Year
DOI
Venue
2019
10.5220/0008377205550560
KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR
Keywords
Field
DocType
Recommendation System, Sequential Pattern, Similarity Measure, Time
Recommender system,Collaborative filtering,Similarity measure,Computer science,Neighbourhood (mathematics),Artificial intelligence,Machine learning
Conference
Volume
Citations 
PageRank 
2
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Nima Joorabloo152.41
Mahdi Jalili231437.98
Yongli Ren300.34