基于潛在主題的混合上下文推薦算法

李平; 張路遙; 曹霞; 胡檢華

doi:10.11999/JEIT170623

基于潛在主題的混合上下文推薦算法

doi: 10.11999/JEIT170623 cstr: 32379.14.JEIT170623

2.
(長沙理工大學計算機與通信工程學院長沙 410114)

基金項目:

湖南省教育廳資助重點項目(14A004)

計量
- 文章訪問數(shù): 1656
- HTML全文瀏覽量: 241
- PDF下載量: 194
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-06-28
- 修回日期: 2017-11-20
- 刊出日期: 2018-04-19

Hybrid Context Recommendation Algorithm Based on Latent Topic

2.
(School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

Funds:

The Scientific Research Fund of Hunan Provincial Education Department (14A004)

摘要

摘要: 針對單個環(huán)境上下文中項目訪問記錄稀疏的問題，推薦系統(tǒng)難以獲取與當前環(huán)境上下文關聯(lián)的用戶偏好。該文設計了一種新的上下文關聯(lián)性推薦(CTRR)算法。CTRR算法通過CTRR_LDA模型求解推薦項目出現(xiàn)在特定環(huán)境上下文的概率，并結合上下文后過濾推薦算法，對用戶進行推薦。CTRR_LDA模型是在(LDA)模型的基礎上，結合環(huán)境上下文和項目特征上下文，提出的項目與環(huán)境上下文的關聯(lián)概率模型。該模型將環(huán)境上下文劃分為多個環(huán)境上下文因子，每個環(huán)境上下文因子表示為K維的主題分布，挖掘環(huán)境上下文因子中項目出現(xiàn)的潛在主題特征。利用LDOS-CoMoDa網(wǎng)站上真實的電影數(shù)據(jù)集進行實驗，驗證了算法的可靠性。
- 推薦 /
- 關聯(lián)概率 /
- 潛在主題 /
- 環(huán)境上下文 /
- 項目特征上下文
Abstract: In the recommendation system, a critical challenge is that individual environment context log may not contain sufficient item access records for mining his/her environment context preferences. This paper designs a Contextual Topic-based Relevance Recommendation (CTRR) algorithm. The CTRR algorithm uses the CTRR_LDA model and a postfiltering strategy to recommend items to users in a specific environment context. CTRR_LDA is an improved LDA model, which combines environment contexts and item feature contexts to calculate the probability of the item appeared. In this model, the environment context is divided into multiple environment context factors. Each environment context factor can be expressed as a K-dimensional topic distribution. Then the CTRR_LDA model is used to mine the latent topic of the items in each environment context factor. According to the experimental results on the LDOS-CoMoDa datasets, the reliability of algorithm is validated in the context-aware recommendation scenario.
- Recommendation /
- Relevance probability /
- Latent topic /
- Environment context /
- Item feature context

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參考文獻(18)

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