Hybrid Context Recommendation Algorithm Based on Latent Topic

LI Ping; ZHANG Luyao; CAO Xia; HU Jianhua

doi:10.11999/JEIT170623

Volume 40 Issue 4

Apr. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(4): 957-963

LI Ping, ZHANG Luyao, CAO Xia, HU Jianhua. Hybrid Context Recommendation Algorithm Based on Latent Topic[J]. Journal of Electronics & Information Technology, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623

Citation:

LI Ping, ZHANG Luyao, CAO Xia, HU Jianhua. Hybrid Context Recommendation Algorithm Based on Latent Topic[J]. Journal of Electronics & Information Technology, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623

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Hybrid Context Recommendation Algorithm Based on Latent Topic

doi: 10.11999/JEIT170623

1.
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)

Received Date: 2017-06-28
Rev Recd Date: 2017-11-20
Publish Date: 2018-04-19

Abstract

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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References(18)

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