.png){kind=icon}
返回
A sequence-based and context modelling framework for recommendation
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2021.114665.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Since the last decade, data collection is becoming more pervasive, passive and easier to perform. This is resulting in the rise of data wherein a user performs some activities in a sequence, such as locations visited, physical activities performed, and modes of transport taken. In such cases, activities are often performed in a particular order, and each activity in turn may influence the subsequent activities to be performed. Moreover, such activities may be associated with multiple features or contexts, such as location, time, weather, etc. The order encoded in such data, along with the context, capture important information when it comes to modelling the preferences and personal habits of users. Traditional recommender systems, however, typically do not consider the order in which users perform activities and there is little work which considers both sequence and context simultaneously. In this work, a generic recommendation framework is proposed which leverages both sequences and context in user activity data for activity recommendation. To model user activities, a semantic view of the user's past activities as a timeline of activity objects is presented. An essential step in the recommendation process is finding patterns in past activities performed which are closely aligned to the recent activities undertaken by the user. To calculate the distance between timelines, a novel two-level distance metric is presented which calculates distance with respect to the order of the activities as well as the context features associated with each activity occurrence. The efficacy of the proposed activity recommendation framework in various recommendation scenarios, is demonstrated using real-world datasets from multiple domains. Superscript/Subscript Available
Keyword:
Recommender systems
Activity recommendation
Sequence aware recommendation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
Rupture length of the October 9, 1995 Colima‐Jalisco Earthquake (Mw 8) estimated from tsunami dataOAAI
.png){kind=icon}
OAAI分享收藏分享收藏The effects of managerial capabilities on export, FDI and innovation: Evidence from Indian firms
PREAI
分享收藏Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏分享收藏MARROW TRANSPLANTATION FROM HLA-MATCHED UNRELATED DONORS FOR TREATMENT OF HEMATOLOGIC MALIGNANCIESOAAI
OAAI分享收藏
查看更多内容