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| Hybrid Context Recommendation Algorithm Based on Latent Topic |
| LI Ping①② ZHANG Luyao① CAO Xia① HU Jianhua① |
①(School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)
②(Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha 410114, China) |
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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.
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Received: 28 June 2017
Published: 12 December 2017
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| Fund:The Scientific Research Fund of Hunan Provincial Education Department (14A004) |
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Corresponding Authors:
ZHANG Luyao
E-mail: 465888329@qq.com
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