标题: 以协同主题模式与跨领域分析为基础之线上电影推荐方法
Online Movie Recommendation Approach based on Collaborative Topic Modeling and Cross-Domain Analysis
作者: 简巧婷
刘敦仁
Jian, Ciao-Ting
Liu, Duen-Ren
资讯管理研究所
关键字: 电影推荐;矩阵分解;隐含主题模式;协同主题模式;跨领域;多样性;Movie Recommendation;Latent Topic Model;Collaborative Topic Modeling;Association Rules;Cross-domain;Diversity
公开日期: 2017
摘要: 随着互联网的蓬勃发展和新型态的网路资讯与电子商务平台之兴起,越来越多使用者透过网路资讯平台来获取特定主题如生活时尚新闻等资讯。从网路资讯平台可以分析使用者的浏览行为与喜好成功推荐新资讯,以吸引更多使用者并提升资讯平台之流量,是目前网路平台重要发展趋势。然而,资讯平台之多角化发展促使资讯平台提供之相关资讯量爆炸且愈趋复杂,对于用户来说,要寻找自己感兴趣的资讯已经成为一件困难的事情。因此,藉由良好的线上推荐机制来推荐使用者有兴趣之相关资讯,并提高使用者的点击率,是目前电子商务平台IT技术中不可或缺的一环。
本研究整合跨领域资讯来源及喜好多样性分析,研发一个新的线上电影推荐机制,并进行线上推荐评估与比较。然而,随着网站资讯平台规模的扩大,使用者人数和项目数据急遽增加,导致使用者的浏览资料量的极端稀疏性(Data Sparsity),在如此的情况下,传统推荐方法的推荐成效较不佳。因此,本研究将以关联规则(Association Rules)及隐含主题模式(LDA)探勘为基础,进行跨领域分析,并结合协同主题模式(CTM),来预测使用者历史与线上电影喜好,并分析其喜好多样性或单一性程度,研发新的线上电影推荐方法,以解决资料稀疏性问题。实验结果显示,本研究所提出的方法能有效改善cold-start问题,并且提升使用者的电影点击率。
With the rapid development of the Internet and the rise of new types of news websites with e-commerce portals, more and more users obtain specific topics online information. Successfully information recommendation to users by analyzing users’ browsing behaviors and preferences in the web-based platform can attract more users and enhance the information flow of platform, which is an important trend of the current online worlds. However, information provided by news websites is exploding and becoming more complicated. Therefore, it is an indispensable part of IT technology for e-commerce platforms to deploy appropriate online recommendation methods to improve the users’ click-through rates.
In this research, we conduct cross-domain and diversity analysis of user preferences to develop novel online movie recommendation methods and evaluated online recommendation results. Specifically, association rule mining is conducted on user browsing news and moves to find the latent associations between news and movies. A novel online recommendation approach is proposed to predict user preferences for movies based on Latent Dirichlet Allocation, Collaborative Topic Modeling and the diversity of recommendations. The experimental results show that the proposed approach can improve the cold-start problem and enhance the click-through rate of movies.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453418
http://hdl.handle.net/11536/141876
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